[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-benedekrozemberczki--awesome-gradient-boosting-papers":3,"tool-benedekrozemberczki--awesome-gradient-boosting-papers":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",160784,2,"2026-04-19T11:32:54",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",109154,"2026-04-18T11:18:24",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":77,"owner_email":78,"owner_twitter":79,"owner_website":80,"owner_url":81,"languages":82,"stars":87,"forks":88,"last_commit_at":89,"license":90,"difficulty_score":91,"env_os":92,"env_gpu":93,"env_ram":93,"env_deps":94,"category_tags":97,"github_topics":98,"view_count":32,"oss_zip_url":80,"oss_zip_packed_at":80,"status":17,"created_at":119,"updated_at":120,"faqs":121,"releases":122},9656,"benedekrozemberczki\u002Fawesome-gradient-boosting-papers","awesome-gradient-boosting-papers","A curated list of gradient boosting research papers with implementations. ","awesome-gradient-boosting-papers 是一个精心整理的梯度提升（Gradient Boosting）学术研究资源库，汇集了来自 NeurIPS、ICML、CVPR、KDD 等顶级会议的最新论文及其配套代码实现。\n\n在机器学习领域，梯度提升算法虽应用广泛，但相关研究分散且复现门槛较高。该资源库有效解决了研究者难以快速追踪前沿进展、开发者寻找高质量参考代码耗时费力的问题。它将原本零散的学术成果系统化，提供了从理论创新到工程落地的完整链路，涵盖计算机视觉、自然语言处理、金融风控及公平性回归等多个垂直场景。\n\n这份清单特别适合人工智能研究人员、算法工程师以及数据科学家使用。无论是希望了解 2025 年最新突破（如联邦学习下的欺诈检测、区间删失数据建模），还是需要为项目寻找可靠的基线模型，用户都能在此快速定位所需资源。其独特亮点在于不仅收录论文，还严格筛选并链接了开源代码，极大地降低了复现难度。此外，作为\"Awesome\"系列的一部分，它与决策树、图分类等其他专题资源库形成了良好的知识互补，是深入探索集成学习领域的高效入口。","# Awesome Gradient Boosting Research Papers\n[![Awesome](https:\u002F\u002Fcdn.rawgit.com\u002Fsindresorhus\u002Fawesome\u002Fd7305f38d29fed78fa85652e3a63e154dd8e8829\u002Fmedia\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome) [![PRs Welcome](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-welcome-brightgreen.svg?style=flat-square)](http:\u002F\u002Fmakeapullrequest.com) ![License](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fbenedekrozemberczki\u002Fawesome-gradient-boosting-papers.svg?color=blue) [![repo size](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Frepo-size\u002Fbenedekrozemberczki\u002Fawesome-gradient-boosting-papers.svg)](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-gradient-boosting-papers\u002Farchive\u002Fmaster.zip) [![benedekrozemberczki](https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002Fbenrozemberczki?style=social&logo=twitter)](https:\u002F\u002Ftwitter.com\u002Fintent\u002Ffollow?screen_name=benrozemberczki)\n\u003Cp align=\"center\">\n  \u003Cimg width=\"450\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fbenedekrozemberczki_awesome-gradient-boosting-papers_readme_06d507204868.gif\">\n\u003C\u002Fp>\n\n--------------------------------\n\nA curated list of gradient and adaptive boosting papers with implementations from the following conferences:\n\n- Machine learning\n   * [NeurIPS](https:\u002F\u002Fnips.cc\u002F) \n   * [ICML](https:\u002F\u002Ficml.cc\u002F) \n   * [ICLR](https:\u002F\u002Ficlr.cc\u002F)\n- Computer vision\n   * [CVPR](http:\u002F\u002Fcvpr2019.thecvf.com\u002F)\n   * [ICCV](http:\u002F\u002Ficcv2019.thecvf.com\u002F)\n   * [ECCV](https:\u002F\u002Feccv2018.org\u002F)\n- Natural language processing\n   * [ACL](http:\u002F\u002Fwww.acl2019.org\u002FEN\u002Findex.xhtml)\n   * [NAACL](https:\u002F\u002Fnaacl2019.org\u002F)\n   * [EMNLP](https:\u002F\u002Fwww.emnlp-ijcnlp2019.org\u002F) \n- Data\n   * [KDD](https:\u002F\u002Fwww.kdd.org\u002F)\n   * [CIKM](http:\u002F\u002Fwww.cikmconference.org\u002F)   \n   * [ICDM](http:\u002F\u002Ficdm2019.bigke.org\u002F)\n   * [SDM](https:\u002F\u002Fwww.siam.org\u002FConferences\u002FCM\u002FConference\u002Fsdm19)   \n   * [PAKDD](http:\u002F\u002Fpakdd2019.medmeeting.org)\n   * [PKDD\u002FECML](http:\u002F\u002Fecmlpkdd2019.org)\n   * [RECSYS](https:\u002F\u002Frecsys.acm.org\u002F)\n   * [SIGIR](https:\u002F\u002Fsigir.org\u002F)\n   * [WWW](https:\u002F\u002Fwww2019.thewebconf.org\u002F)\n   * [WSDM](www.wsdm-conference.org) \n- Artificial intelligence\n   * [AAAI](https:\u002F\u002Fwww.aaai.org\u002F)\n   * [AISTATS](https:\u002F\u002Fwww.aistats.org\u002F)\n   * [ICANN](https:\u002F\u002Fe-nns.org\u002Ficann2019\u002F)   \n   * [IJCAI](https:\u002F\u002Fwww.ijcai.org\u002F)\n   * [UAI](http:\u002F\u002Fwww.auai.org\u002F)\n\nSimilar collections about [graph classification](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-graph-classification), [classification\u002Fregression tree](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-decision-tree-papers), [fraud detection](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-fraud-detection-papers), [Monte Carlo tree search](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-monte-carlo-tree-search-papers), and [community detection](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-community-detection) papers with implementations.\n\n\n## 2025\n\n- **Free Lunch in the Forest: Functionally-Identical Pruning of Boosted Tree Ensembles (AAAI 2025)**\n  - Youssouf Emine, Alexandre Forel, Idriss Malek, Thibaut Vidal\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2408.16167)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Feminyous\u002Ffipe)\n\n- **Supervised Score-Based Modeling by Gradient Boosting (AAAI 2025)**\n  - Changyuan Zhao, Hongyang Du, Guangyuan Liu, Dusit Niyato\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2411.01159)\n\n- **Additive Model Boosting: New Insights and Pathologies (AISTATS 2025)**\n  - Rickmer Schulte, David Rügamer\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2503.05538)\n\n- **FairRegBoost: An End-to-End Data Processing Framework for Fair and Scalable Regression (CIKM 2025)**\n  - Nico Lässig, Melanie Herschel\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3746252.3761277)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002FNicoLaessig\u002Ffairregboost)\n\n- **Federated Gradient Boosting for Financial Fraud Detection: An Empirical Study in the Banking Sector (CIKM 2025)**\n  - Dae-Young Park, In-Young Ko, Taek-Ho Lee, Junghye Lee\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fepdf\u002F10.1145\u002F3746252.3760891)\n\n- **Boosting Methods for Interval-censored Data with Regression and Classification (ICLR 2025)**\n  - Yuan Bian, Grace Y. Yi, Wenqing He\n  - [[Paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=DzbUL4AJPP)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fkrisyuanbian\u002FL2BOOST-IC)\n\n- **NRGBoost: Energy-Based Generative Boosted Trees (ICLR 2025)**\n  - João Bravo\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2410.03535)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fajoo\u002Fnrgboost)\n\n- **Gradient Boosting Reinforcement Learning (ICML 2025)**\n  - Benjamin Fuhrer, Chen Tessler, Gal Dalal\n  - [[Paper]](https:\u002F\u002Fgithub.com\u002FNVlabs\u002Fgbrl)\n  - [[Code]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2407.08250)\n\n- **Fast Calculation of Feature Contributions in Boosting Trees (UAI 2025)**\n  - Zhongli Jiang, Min Zhang, Dabao Zhang\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2407.03515)\n\n- **Learning Robust XGBoost Ensembles for Regression Tasks (UAI 2025)**\n  - Atri Vivek Sharma, Panagiotis Kouvaros, Alessio Lomuscio\n  - [[Paper]](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Fsharma25a.html)\n\n## 2024\n\n- **Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles (AISTATS 2024)**\n  - Fan Yang, Pierre Le Bodic, Michael Kamp, Mario Boley\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.15691)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Ffyan102\u002FFCOGB)\n\n- **Distributed Boosting: An Enhancing Method on Dataset Distillation (CIKM 2024)**\n  - Xuechao Chen, Wenchao Meng, Peiran Wang, Qihang Zhou\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3627673.3679897)\n\n- **Adversarial Imitation Learning via Boosting (ICLR 2024)**\n  - Jonathan D. Chang, Dhruv Sreenivas, Yingbing Huang, Kianté Brantley, Wen Sun\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2404.08513)\n\n- **Iterative Weak Learnability and Multiclass AdaBoost (KDD 2024)**\n  - In-Koo Cho, Jonathan A. Libgober, Cheng Ding\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671842)\n\n- **Uplift Modelling via Gradient Boosting (KDD 2024)**\n  - Bulat Ibragimov, Anton Vakhrushev\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fepdf\u002F10.1145\u002F3637528.3672019)\n\n- **AdaGMLP: AdaBoosting GNN-to-MLP Knowledge Distillation (KDD 2024)**\n  - Weigang Lu, Ziyu Guan, Wei Zhao, Yaming Yang\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2405.14307)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002FWeigangLu\u002FAdaGMLP-KDD24)\n\n- **PEMBOT: Pareto-Ensembled Multi-task Boosted Trees (KDD 2024)**\n  - Gokul Swamy, Anoop Saladi, Arunita Das, Shobhit Niranjan\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fepdf\u002F10.1145\u002F3637528.3671619)\n## 2023\n\n- **Computing Abductive Explanations for Boosted Trees (AISTATS 2023)**\n  - Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.07740)\n\n- **Boosted Off-Policy Learning (AISTATS 2023)**\n  - Ben London, Levi Lu, Ted Sandler, Thorsten Joachims\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.01148)\n\n- **Variational Boosted Soft Trees (AISTATS 2023)**\n  - Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.10706)\n\n- **Krylov-Bellman boosting: Super-linear policy evaluation in general state spaces (AISTATS 2023)**\n  - Eric Xia, Martin J. Wainwright\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.11377)\n\n- **FairGBM: Gradient Boosting with Fairness Constraints (ICLR 2023)**\n  - André Ferreira Cruz, Catarina Belém, João Bravo, Pedro Saleiro, Pedro Bizarro\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.07850)\n\n- **Gradient Boosting Performs Gaussian Process Inference (ICLR 2023)**\n  - Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.05608)\n\n\n## 2022\n\n- **TransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial Inclusion (AAAI 2022)**\n  - Yiheng Sun, Tian Lu, Cong Wang, Yuan Li, Huaiyu Fu, Jingran Dong, Yunjie Xu\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.02365)\n\n- **A Resilient Distributed Boosting Algorithm (ICML 2022)**\n  - Yuval Filmus, Idan Mehalel, Shay Moran\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.04713)\n\n- **Fast Provably Robust Decision Trees and Boosting (ICML 2022)**\n  - Jun-Qi Guo, Ming-Zhuo Teng, Wei Gao, Zhi-Hua Zhou\n  - [[Paper]](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fguo22h.html)\n\n- **Building Robust Ensembles via Margin Boosting (ICML 2022)**\n  - Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.03362)\n\n- **Retrieval-Based Gradient Boosting Decision Trees for Disease Risk Assessment (KDD 2022)**\n  - Handong Ma, Jiahang Cao, Yuchen Fang, Weinan Zhang, Wenbo Sheng, Shaodian Zhang, Yong Yu\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3534678.3539052)\n\n- **Federated Functional Gradient Boosting (AISTATS 2022)**\n  - Zebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.06972)\n\n- **ExactBoost: Directly Boosting the Margin in Combinatorial and Non-decomposable Metrics (AISTATS 2022)**\n  - Daniel Csillag, Carolina Piazza, Thiago Ramos, João Vitor Romano, Roberto I. Oliveira, Paulo Orenstein\n  - [[Paper]](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fcsillag22a.html)\n\n## 2021\n\n- **Precision-based Boosting (AAAI 2021)**\n  - Mohammad Hossein Nikravan, Marjan Movahedan, Sandra Zilles\n  - [[Paper]](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17105)\n\n- **BNN: Boosting Neural Network Framework Utilizing Limited Amount of Data (CIKM 2021)**\n  - Amit Livne, Roy Dor, Bracha Shapira, Lior Rokach\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3459637.3482414)\n\n- **Unsupervised Domain Adaptation for Static Malware Detection based on Gradient Boosting Trees (CIKM 2021)**\n  - Panpan Qi, Wei Wang, Lei Zhu, See-Kiong Ng\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3459637.3482400)\n\n- **Individually Fair Gradient Boosting (ICLR 2021)**\n  - Alexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.16785)\n\n- **Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees (ICLR 2021)**\n  - Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork\n  - [[Paper]](https:\u002F\u002Ficlr.cc\u002Fvirtual\u002F2021\u002Fspotlight\u002F3536)\n\n- **AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models (ICLR 2021)**\n  - Ke Sun, Zhanxing Zhu, Zhouchen Lin\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.05081)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fdatake\u002FAdaGCN)\n\n- **Uncertainty in Gradient Boosting via Ensembles (ICLR 2021)**\n  - Andrey Malinin, Liudmila Prokhorenkova, Aleksei Ustimenko\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.10562)\n  - \n- **Boost then Convolve: Gradient Boosting Meets Graph Neural Networks (ICLR 2021)**\n  - Sergei Ivanov, Liudmila Prokhorenkova\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.08543)\n\n- **GBHT: Gradient Boosting Histogram Transform for Density Estimation (ICML 2021)**\n  - Jingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.05738)\n\n- **Boosting for Online Convex Optimization (ICML 2021)**\n  - Elad Hazan, Karan Singh\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.09305)\n\n- **Accuracy, Interpretability, and Differential Privacy via Explainable Boosting (ICML 2021)**\n  - Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.09680)\n\n- **SGLB: Stochastic Gradient Langevin Boosting (ICML 2021)**\n  - Aleksei Ustimenko, Liudmila Prokhorenkova\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.07248)\n\n- **Self-boosting for Feature Distillation (IJCAI 2021)**\n  - Yulong Pei, Yanyun Qu, Junping Zhang\n  - [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F131)\n\n- **Boosting Variational Inference With Locally Adaptive Step-Sizes (IJCAI 2021)**\n  - Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.09240)\n\n- **Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression (KDD 2021)**\n  - Olivier Sprangers, Sebastian Schelter, Maarten de Rijke\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.01682)\n\n- **Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction (KDD 2021)**\n  - Mingcheng Chen, Zhenghui Wang, Zhiyun Zhao, Weinan Zhang, Xiawei Guo, Jian Shen, Yanru Qu, Jieli Lu, Min Xu, Yu Xu, Tiange Wang, Mian Li, Weiwei Tu, Yong Yu, Yufang Bi, Weiqing Wang, Guang Ning\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.07107)\n\n- **Better Short than Greedy: Interpretable Models through Optimal Rule Boosting (SDM 2021)**\n  - Mario Boley, Simon Teshuva, Pierre Le Bodic, Geoffrey I. Webb\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.08380)\n\n## 2020\n\n- **A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains (AAAI 2020)**\n  - Harsha Kokel, Phillip Odom, Shuo Yang, Sriraam Natarajan\n  - [[Paper]](https:\u002F\u002Fpersonal.utdallas.edu\u002F~sriraam.natarajan\u002FPapers\u002FKokel_AAAI20.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fharshakokel\u002FKiGB)\n\n- **Practical Federated Gradient Boosting Decision Trees (AAAI 2020)**\n  - Qinbin Li, Zeyi Wen, Bingsheng He\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.04206)\n\n- **Privacy-Preserving Gradient Boosting Decision Trees (AAAI 2020)**\n  - Qinbin Li, Zhaomin Wu, Zeyi Wen, Bingsheng He\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.04209)\n  \n- **Accelerating Gradient Boosting Machines (AISTATS 2020)**\n  - Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.08708)\n\n- **Scalable Feature Selection for Multitask Gradient Boosted Trees (AISTATS 2020)**\n  - Cuize Han, Nikhil Rao, Daria Sorokina, Karthik Subbian\n  - [[Paper]](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002Fhan20a.html)\n\n- **Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees (AISTATS 2020)**\n  - Atsushi Nitanda, Taiji Suzuki\n  - [[Paper]](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002Fnitanda20a.html)\n  \n- **Learning Optimal Decision Trees with MaxSAT and its Integration in AdaBoost (IJCAI 2020)**\n  - Hao Hu, Mohamed Siala, Emmanuel Hebrard, Marie-José Huguet\n  - [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F2020\u002F163)\n\n- **MixBoost: Synthetic Oversampling using Boosted Mixup for Handling Extreme Imbalance (ICDM 2020)**\n  - Anubha Kabra, Ayush Chopra, Nikaash Puri, Pinkesh Badjatiya, Sukriti Verma, Piyush Gupta, Balaji Krishnamurthy\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.01571)\n\n- **Boosting for Control of Dynamical Systems (ICML 2020)**\n  - Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.08720)\n\n- **Quantum Boosting (ICML 2020)**\n  - Srinivasan Arunachalam, Reevu Maity\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.05056)\n\n- **Boosted Histogram Transform for Regression (ICML 2020)**\n  - Yuchao Cai, Hanyuan Hang, Hanfang Yang, Zhouchen Lin\n  - [[Paper]](https:\u002F\u002Fproceedings.icml.cc\u002Fstatic\u002Fpaper_files\u002Ficml\u002F2020\u002F2360-Paper.pdf)\n\n- **Boosting Frank-Wolfe by Chasing Gradients (ICML 2020)**\n  - Cyrille W. Combettes, Sebastian Pokutta\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.06369)\n\n- **NGBoost: Natural Gradient Boosting for Probabilistic Prediction (ICML 2020)**\n  - Tony Duan, Avati Anand, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1910.03225)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fstanfordmlgroup\u002Fngboost)\n  \n- **Online Agnostic Boosting via Regret Minimization (NeurIPS 2020)**\n  - Nataly Brukhim, Xinyi Chen, Elad Hazan, Shay Moran\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.01150)\n  \n- **Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst Case Rates (NeurIPS 2020)**\n  - Kaiwen Zhou, Anthony Man-Cho So, James Cheng\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.12061)\n\n- **Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks (NeurIPS 2020)**\n  - Kenta Oono, Taiji Suzuki\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.08550)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fdelta2323\u002FGB-GNN)\n  \n- **Gradient Boosted Normalizing Flows (NeurIPS 2020)**\n  - Robert Giaquinto, Arindam Banerjee\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.11896)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Frobert-giaquinto\u002Fgradient-boosted-normalizing-flows)\n\n- **HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems (WSDM 2020)**\n  - Lucas Vinh Tran, Yi Tay, Shuai Zhang, Gao Cong, Xiaoli Li\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.01703)\n\n## 2019\n\n- **Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME (AAAI 2019)**\n  - Farhad Shakerin, Gopal Gupta\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.00629)\n\n- **Verifying Robustness of Gradient Boosted Models (AAAI 2019)**\n  - Gil Einziger, Maayan Goldstein, Yaniv Sa'ar, Itai Segall\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.10991.pdf)\n\n- **Online Multiclass Boosting with Bandit Feedback (AISTATS 2019)**\n  - Daniel T. Zhang, Young Hun Jung, Ambuj Tewari\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.05290)\n  \n- **AdaFair: Cumulative Fairness Adaptive Boosting (CIKM 2019)**\n  - Vasileios Iosifidis, Eirini Ntoutsi\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.08982)\n\n- **Interpretable MTL from Heterogeneous Domains using Boosted Tree (CIKM 2019)**\n  - Ya-Lin Zhang, Longfei Li\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3357384.3358072)\n\n- **Adversarial Training of Gradient-Boosted Decision Trees (CIKM 2019)**\n  - Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei\n  - [[Paper]](https:\u002F\u002Fwww.dais.unive.it\u002F~calzavara\u002Fpapers\u002Fcikm19.pdf)\n  \n- **Fair Adversarial Gradient Tree Boosting (ICDM 2019)**\n  - Vincent Grari, Boris Ruf, Sylvain Lamprier, Marcin Detyniecki\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.05369)\n\n- **Boosted Density Estimation Remastered (ICML 2019)**\n  - Zac Cranko, Richard Nock\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.08178)\n\n- **Lossless or Quantized Boosting with Integer Arithmetic (ICML 2019)**\n  - Richard Nock, Robert C. Williamson\n  - [[Paper]](http:\u002F\u002Fproceedings.mlr.press\u002Fv97\u002Fnock19a.html)\n\n- **Optimal Minimal Margin Maximization with Boosting (ICML 2019)**\n  - Alexander Mathiasen, Kasper Green Larsen, Allan Grønlund\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.10789)\n\n- **Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number (ICML 2019)**\n  - Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.06754)\n  \n- **Boosting for Comparison-Based Learning (IJCAI 2019)**\n  - Michaël Perrot, Ulrike von Luxburg\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.13333)\n\n- **AugBoost: Gradient Boosting Enhanced with Step-Wise Feature Augmentation (IJCAI 2019)**\n  - Philip Tannor, Lior Rokach\n  - [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F0493.pdf)\n\n- **Gradient Boosting with Piece-Wise Linear Regression Trees (IJCAI 2019)**\n  - Yu Shi, Jian Li, Zhize Li\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.05640)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002FGBDT-PL\u002FGBDT-PL)\n  \n- **SpiderBoost and Momentum: Faster Variance Reduction Algorithms (NeurIPS 2019)**\n  - Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh\n  - [[Paper]](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8511-spiderboost-and-momentum-faster-variance-reduction-algorithms)\n\n- **Faster Boosting with Smaller Memory (NeurIPS 2019)**\n  - Julaiti Alafate, Yoav Freund\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.09047)\n\n- **Regularized Gradient Boosting (NeurIPS 2019)**\n  - Corinna Cortes, Mehryar Mohri, Dmitry Storcheus\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8784-regularized-gradient-boosting)\n\n- **Margin-Based Generalization Lower Bounds for Boosted Classifiers (NeurIPS 2019)**\n  - Allan Grønlund, Lior Kamma, Kasper Green Larsen, Alexander Mathiasen, Jelani Nelson\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.12518)\n\n- **Minimal Variance Sampling in Stochastic Gradient Boosting (NeurIPS 2019)**\n  - Bulat Ibragimov, Gleb Gusev\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F9645-minimal-variance-sampling-in-stochastic-gradient-boosting)\n\n- **Universal Boosting Variational Inference (NeurIPS 2019)**\n  - Trevor Campbell, Xinglong Li\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.01235)\n  \n- **Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks (NeurIPS 2019)**\n  - Maksym Andriushchenko, Matthias Hein\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.03526)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fmax-andr\u002Fprovably-robust-boosting)\n  \n- **Block-distributed Gradient Boosted Trees (SIGIR 2019)**\n  - Theodore Vasiloudis, Hyunsu Cho, Henrik Boström\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.10522)\n  \n- **Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning (SIGIR 2019)**\n  - Claudio Lucchese, Franco Maria Nardini, Rama Kumar Pasumarthi, Sebastian Bruch, Michael Bendersky, Xuanhui Wang, Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke\n  - [[Paper]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F334579610_Learning_to_Rank_in_Theory_and_Practice_From_Gradient_Boosting_to_Neural_Networks_and_Unbiased_Learning)\n\n## 2018\n- **Boosted Generative Models (AAAI 2018)**\n  - Aditya Grover, Stefano Ermon\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1702.08484.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fermongroup\u002Fbgm)\n\n- **Boosting Variational Inference: an Optimization Perspective (AISTATS 2018)**\n  - Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.01733)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fratschlab\u002Fboosting-bbvi)\n\n- **Online Boosting Algorithms for Multi-label Ranking (AISTATS 2018)**\n  - Young Hun Jung, Ambuj Tewari\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.08079)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fyhjung88\u002FOnlineMLRBoostingWithVFDT)\n\n- **DualBoost: Handling Missing Values with Feature Weights and Weak Classifiers that Abstain (CIKM 2018)**\n  - Weihong Wang, Jie Xu, Yang Wang, Chen Cai, Fang Chen\n  - [[Paper]](http:\u002F\u002Fdelivery.acm.org\u002F10.1145\u002F3270000\u002F3269319\u002Fp1543-wang.pdf?ip=129.215.164.203&id=3269319&acc=ACTIVE%20SERVICE&key=C2D842D97AC95F7A%2EEB9E991028F4E1F1%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&__acm__=1558633895_f01b39fd47b943fd01eade763a397e04)\n\n- **Functional Gradient Boosting based on Residual Network Perception (ICML 2018)**\n  - Atsushi Nitanda, Taiji Suzuki\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.09031)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fanitan0925\u002FResFGB)\n\n- **Finding Influential Training Samples for Gradient Boosted Decision Trees (ICML 2018)**\n  - Boris Sharchilev, Yury Ustinovskiy, Pavel Serdyukov, Maarten de Rijke\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.06640)\n\n- **Learning Deep ResNet Blocks Sequentially using Boosting Theory (ICML 2018)**\n  - Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.04964)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002FJordanAsh\u002Fboostresnet)\n\n- **UCBoost: A Boosting Approach to Tame Complexity and Optimality for Stochastic Bandits (IJCAI 2018)**\n  - Fang Liu, Sinong Wang, Swapna Buccapatnam, Ness B. Shroff\n  - [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2018\u002F0338.pdf)\n  - [[Code]](https:\u002F\u002Fsmpybandits.github.io\u002Fdocs\u002FPolicies.UCBoost.html)\n\n- **Adaboost with Auto-Evaluation for Conversational Models (IJCAI 2018)**\n  - Juncen Li, Ping Luo, Ganbin Zhou, Fen Lin, Cheng Niu\n  - [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2018\u002F0580.pdf)\n\n- **Ensemble Neural Relation Extraction with Adaptive Boosting (IJCAI 2018)**\n  - Dongdong Yang, Senzhang Wang, Zhoujun Li\n  - [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2018\u002F0630.pdf)\n\n- **CatBoost: Unbiased Boosting with Categorical Features (NIPS 2018)**\n  - Liudmila Ostroumova Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7898-catboost-unbiased-boosting-with-categorical-features.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fcatboost\u002Fcatboost)\n\n- **Multitask Boosting for Survival Analysis with Competing Risks (NIPS 2018)**\n  - Alexis Bellot, Mihaela van der Schaar\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7413-multitask-boosting-for-survival-analysis-with-competing-risks)\n\n- **Multi-Layered Gradient Boosting Decision Trees (NIPS 2018)**\n  - Ji Feng, Yang Yu, Zhi-Hua Zhou\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7614-multi-layered-gradient-boosting-decision-trees.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fkingfengji\u002FmGBDT)\n\n- **Boosted Sparse and Low-Rank Tensor Regression (NIPS 2018)**\n  - Lifang He, Kun Chen, Wanwan Xu, Jiayu Zhou, Fei Wang\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.01158)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002FLifangHe\u002FNeurIPS18_SURF)\n  \n- **Selective Gradient Boosting for Effective Learning to Rank (SIGIR 2018)**\n  - Claudio Lucchese, Franco Maria Nardini, Raffaele Perego, Salvatore Orlando, Salvatore Trani\n  - [[Paper]](http:\u002F\u002Fquickrank.isti.cnr.it\u002Fselective-data\u002Fselective-SIGIR2018.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fhpclab\u002Fquickrank\u002Fblob\u002Fmaster\u002Fdocumentation\u002Fselective.md)\n\n## 2017\n- **Boosting for Real-Time Multivariate Time Series Classification (AAAI 2017)**\n  - Haishuai Wang, Jun Wu\n  - [[Paper]](https:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI17\u002Fpaper\u002Fdownload\u002F14852\u002F14241)\n\n- **Cross-Domain Sentiment Classification via Topic-Related TrAdaBoost (AAAI 2017)**\n  - Xingchang Huang, Yanghui Rao, Haoran Xie, Tak-Lam Wong, Fu Lee Wang\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F826c\u002Fc83d98a5c4c7dcc02be1f4dd9c27e2b99670.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fxchhuang\u002Fcross-domain-sentiment-classification)\n\n- **Extreme Gradient Boosting and Behavioral Biometrics (AAAI 2017)**\n  - Benjamin Manning\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F8c6e\u002F6c887d6d47dda3f0c73297fd4da516fef1ee.pdf)\n\n- **FeaBoost: Joint Feature and Label Refinement for Semantic Segmentation (AAAI 2017)**\n  - Yulei Niu, Zhiwu Lu, Songfang Huang, Xin Gao, Ji-Rong Wen\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fd566\u002F73be998b3ed38ccbb53551e38758ae8cfc9d.pdf)\n\n- **Boosting Complementary Hash Tables for Fast Nearest Neighbor Search (AAAI 2017)**\n  - Xianglong Liu, Cheng Deng, Yadong Mu, Zhujin Li\n  - [[Paper]](https:\u002F\u002Faaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI17\u002Fpaper\u002Fview\u002F14336)\n\n- **Gradient Boosting on Stochastic Data Streams (AISTATS 2017)**\n  - Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.00377)\n\n- **BoostVHT: Boosting Distributed Streaming Decision Trees (CIKM 2017)**\n  - Theodore Vasiloudis, Foteini Beligianni, Gianmarco De Francisci Morales\n  - [[Paper]](https:\u002F\u002Fmelmeric.files.wordpress.com\u002F2010\u002F05\u002Fboostvht-boosting-distributed-streaming-decision-trees.pdf)\n\n- **Fast Boosting Based Detection Using Scale Invariant Multimodal Multiresolution Filtered Features (CVPR 2017)**\n  - Arthur Daniel Costea, Robert Varga, Sergiu Nedevschi\n  - [[Paper]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2017\u002Fpapers\u002FCostea_Fast_Boosting_Based_CVPR_2017_paper.pdf)\n\n- **BIER - Boosting Independent Embeddings Robustly (ICCV 2017)**\n  - Michael Opitz, Georg Waltner, Horst Possegger, Horst Bischof\n  - [[Paper]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2017\u002Fpapers\u002FOpitz_BIER_-_Boosting_ICCV_2017_paper.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fmop\u002Fbier)\n\n- **An Analysis of Boosted Linear Classifiers on Noisy Data with Applications to Multiple-Instance Learning (ICDM 2017)**\n  - Rui Liu, Soumya Ray\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8215501)\n\n- **Variational Boosting: Iteratively Refining Posterior Approximations (ICML 2017)**\n  - Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.06585)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fandymiller\u002Fvboost)\n\n- **Boosted Fitted Q-Iteration (ICML 2017)**\n  - Samuele Tosatto, Matteo Pirotta, Carlo D'Eramo, Marcello Restelli\n  - [[Paper]](http:\u002F\u002Fproceedings.mlr.press\u002Fv70\u002Ftosatto17a.html)\n\n- **A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical Proficiency (ICML 2017)**\n  - Ron Appel, Pietro Perona\n  - [[Paper]](http:\u002F\u002Fproceedings.mlr.press\u002Fv70\u002Fappel17a.html)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002FGuillaumeCollin\u002FA-Simple-Multi-Class-Boosting-Framework-with-Theoretical-Guarantees-and-Empirical-Proficiency)\n\n- **Gradient Boosted Decision Trees for High Dimensional Sparse Output (ICML 2017)**\n  - Si Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon, Cho-Jui Hsieh\n  - [[Paper]](http:\u002F\u002Fproceedings.mlr.press\u002Fv70\u002Fsi17a.html)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fspringdaisy\u002FGBDT)\n\n- **Local Topic Discovery via Boosted Ensemble of Nonnegative Matrix Factorization (IJCAI 2017)**\n  - Sangho Suh, Jaegul Choo, Joonseok Lee, Chandan K. Reddy\n  - [[Paper]](http:\u002F\u002Fdmkd.cs.vt.edu\u002Fpapers\u002FIJCAI17.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FBoostedFactorization)\n\n- **Boosted Zero-Shot Learning with Semantic Correlation Regularization (IJCAI 2017)**\n  - Te Pi, Xi Li, Zhongfei (Mark) Zhang\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.08008)\n\n- **BDT: Gradient Boosted Decision Tables for High Accuracy and Scoring Efficiency (KDD 2017)**\n  - Yin Lou, Mikhail Obukhov\n  - [[Paper]](https:\u002F\u002Fyinlou.github.io\u002Fpapers\u002Flou-kdd17.pdf)\n  \n- **CatBoost: Gradient Boosting with Categorical Features Support (NIPS 2017)**\n  - Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.11363)\n  - [[Code]](https:\u002F\u002Fcatboost.ai\u002F)\n\n- **Cost Efficient Gradient Boosting (NIPS 2017)**\n  - Sven Peter, Ferran Diego, Fred A. Hamprecht, Boaz Nadler\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F6753-cost-efficient-gradient-boosting)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fsvenpeter42\u002FLightGBM-CEGB)\n\n- **AdaGAN: Boosting Generative Models (NIPS 2017)**\n  - Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.02386)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Ftolstikhin\u002Fadagan)\n\n- **LightGBM: A Highly Efficient Gradient Boosting Decision Tree (NIPS 2017)**\n  - Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree)\n  - [[Code]](https:\u002F\u002Flightgbm.readthedocs.io\u002Fen\u002Flatest\u002F)\n\n- **Early Stopping for Kernel Boosting Algorithms: A General Analysis with Localized Complexities (NIPS 2017)**\n  - Yuting Wei, Fanny Yang, Martin J. Wainwright\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.01543)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Ffanny-yang\u002FEarlyStoppingRKHS)\n\n- **Online Multiclass Boosting (NIPS 2017)**\n  - Young Hun Jung, Jack Goetz, Ambuj Tewari\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F6693-online-multiclass-boosting.pdf)\n\n- **Stacking Bagged and Boosted Forests for Effective Automated Classification (SIGIR 2017)**\n  - Raphael R. Campos, Sérgio D. Canuto, Thiago Salles, Clebson C. A. de Sá, Marcos André Gonçalves\n  - [[Paper]](https:\u002F\u002Fhomepages.dcc.ufmg.br\u002F~rcampos\u002Fpapers\u002Fsigir2017\u002Fappendix.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fraphaelcampos\u002Fstacking-bagged-boosted-forests)\n\n- **GB-CENT: Gradient Boosted Categorical Embedding and Numerical Trees (WWW 2017)**\n  - Qian Zhao, Yue Shi, Liangjie Hong\n  - [[Paper]](http:\u002F\u002Fpapers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com\u002Fproceedings\u002Fp1311.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fgrouplens\u002Fsamantha)\n\n## 2016\n- **Group Cost-Sensitive Boosting for Multi-Resolution Pedestrian Detection (AAAI 2016)**\n  - Chao Zhu, Yuxin Peng\n  - [[Paper]](https:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI16\u002Fpaper\u002FviewFile\u002F11898\u002F12146)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fnnikolaou\u002FCost-sensitive-Boosting-Tutorial)\n\n- **Communication Efficient Distributed Agnostic Boosting (AISTATS 2016)**\n  - Shang-Tse Chen, Maria-Florina Balcan, Duen Horng Chau\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1506.06318)\n\n- **Logistic Boosting Regression for Label Distribution Learning (CVPR 2016)**\n  - Chao Xing, Xin Geng, Hui Xue\n  - [[Paper]](https:\u002F\u002Fzpascal.net\u002Fcvpr2016\u002FXing_Logistic_Boosting_Regression_CVPR_2016_paper.pdf)\n\n- **Structured Regression Gradient Boosting (CVPR 2016)**\n  - Ferran Diego, Fred A. Hamprecht\n  - [[Paper]](https:\u002F\u002Fhci.iwr.uni-heidelberg.de\u002Fsites\u002Fdefault\u002Ffiles\u002Fpublications\u002Ffiles\u002F1037872734\u002Fdiego_16_structured.pdf)\n  \n- **L-EnsNMF: Boosted Local Topic Discovery via Ensemble of Nonnegative Matrix Factorization (ICDM 2016)**\n  - Sangho Suh, Jaegul Choo, Joonseok Lee, Chandan K. Reddy\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7837872)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FBoostedFactorization)\n\n- **Meta-Gradient Boosted Decision Tree Model for Weight and Target Learning (ICML 2016)**\n  - Yury Ustinovskiy, Valentina Fedorova, Gleb Gusev, Pavel Serdyukov\n  - [[Paper]](http:\u002F\u002Fproceedings.mlr.press\u002Fv48\u002Fustinovskiy16.html)\n\n- **Generalized Dictionary for Multitask Learning with Boosting (IJCAI 2016)**\n  - Boyu Wang, Joelle Pineau\n  - [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F16\u002FPapers\u002F299.pdf)\n\n- **Self-Paced Boost Learning for Classification (IJCAI 2016)**\n  - Te Pi, Xi Li, Zhongfei Zhang, Deyu Meng, Fei Wu, Jun Xiao, Yueting Zhuang\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F31b6\u002Fab4a0771d5b7405cacdd12c398b1c832729d.pdf)\n\n- **Interactive Martingale Boosting (IJCAI 2016)**\n  - Ashish Kulkarni, Pushpak Burange, Ganesh Ramakrishnan\n  - [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F16\u002FPapers\u002F124.pdf)\n\n- **Optimal and Adaptive Algorithms for Online Boosting (IJCAI 2016)**\n  - Alina Beygelzimer, Satyen Kale, Haipeng Luo\n  - [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F16\u002FPapers\u002F614.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002FVowpalWabbit\u002Fvowpal_wabbit\u002Fblob\u002Fmaster\u002Fvowpalwabbit\u002Fboosting.cc)\n\n- **Rating-Boosted Latent Topics: Understanding Users and Items with Ratings and Reviews (IJCAI 2016)**\n  - Yunzhi Tan, Min Zhang, Yiqun Liu, Shaoping Ma\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fdb63\u002F89e0ca49ec0e4686e40604e7489cb4c0729d.pdf)\n\n- **XGBoost: A Scalable Tree Boosting System (KDD 2016)**\n  - Tianqi Chen, Carlos Guestrin\n  - [[Paper]](https:\u002F\u002Fwww.kdd.org\u002Fkdd2016\u002Fpapers\u002Ffiles\u002Frfp0697-chenAemb.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fdmlc\u002Fxgboost)\n\n- **Boosted Decision Tree Regression Adjustment for Variance Reduction in Online Controlled Experiments (KDD 2016)**\n  - Alexey Poyarkov, Alexey Drutsa, Andrey Khalyavin, Gleb Gusev, Pavel Serdyukov\n  - [[Paper]](https:\u002F\u002Fwww.kdd.org\u002Fkdd2016\u002Fpapers\u002Ffiles\u002Fadf0653-poyarkovA.pdf)\n\n- **Boosting with Abstention (NIPS 2016)**\n  - Corinna Cortes, Giulia DeSalvo, Mehryar Mohri\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F6336-boosting-with-abstention)\n\n- **SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques (NIPS 2016)**\n  - Elad Richardson, Rom Herskovitz, Boris Ginsburg, Michael Zibulevsky\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F6109-seboost-boosting-stochastic-learning-using-subspace-optimization-techniques.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Feladrich\u002Fseboost)\n\n- **Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition (NIPS 2016)**\n  - Shizhong Han, Zibo Meng, Ahmed-Shehab Khan, Yan Tong\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.05395)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fsjsingh91\u002FIB-CNN)\n  \n- **Generalized BROOF-L2R: A General Framework for Learning to Rank Based on Boosting and Random Forests (SIGIR 2016)**\n  - Clebson C. A. de Sá, Marcos André Gonçalves, Daniel Xavier de Sousa, Thiago Salles\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2911540)\n\n## 2015\n\n- **Online Boosting Algorithms for Anytime Transfer and Multitask Learning (AAAI 2015)**\n  - Boyu Wang, Joelle Pineau\n  - [[Paper]](https:\u002F\u002Fwww.cs.mcgill.ca\u002F~jpineau\u002Ffiles\u002Fbwang-aaai15.pdf)\n\n- **A Boosted Multi-Task Model for Pedestrian Detection with Occlusion Handling (AAAI 2015)**\n  - Chao Zhu, Yuxin Peng\n  - [[Paper]](https:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI15\u002Fpaper\u002FviewFile\u002F9879\u002F9825)\n\n- **Efficient Second-Order Gradient Boosting for Conditional Random Fields (AISTATS 2015)**\n  - Tianqi Chen, Sameer Singh, Ben Taskar, Carlos Guestrin\n  - [[Paper]](http:\u002F\u002Fproceedings.mlr.press\u002Fv38\u002Fchen15b.html)\n\n- **Tumblr Blog Recommendation with Boosted Inductive Matrix Completion (CIKM 2015)**\n  - Donghyuk Shin, Suleyman Cetintas, Kuang-Chih Lee, Inderjit S. Dhillon\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2806578)\n\n- **Basis mapping based boosting for object detection (CVPR 2015)**\n  - Haoyu Ren, Ze-Nian Li\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7298766)\n\n- **Tracking-by-Segmentation with Online Gradient Boosting Decision Tree (ICCV 2015)**\n  - Jeany Son, Ilchae Jung, Kayoung Park, Bohyung Han\n  - [[Paper]](https:\u002F\u002Fwww.cv-foundation.org\u002Fopenaccess\u002Fcontent_iccv_2015\u002Fpapers\u002FSon_Tracking-by-Segmentation_With_Online_ICCV_2015_paper.pdf)\n  - [[Code]](http:\u002F\u002Fcvlab.postech.ac.kr\u002Fresearch\u002Fogbdt_track\u002F)\n\n- **Learning to Boost Filamentary Structure Segmentation (ICCV 2015)**\n  - Lin Gu, Li Cheng\n  - [[Paper]](https:\u002F\u002Fisg.nist.gov\u002FBII_2015\u002FwebPages\u002Fpages\u002F2015_BII_program\u002FPDFs\u002FDay_3\u002FSession_9\u002FAbstract_Gu_Lin.pdf)\n\n- **Optimal and Adaptive Algorithms for Online Boosting (ICML 2015)**\n  - Alina Beygelzimer, Satyen Kale, Haipeng Luo\n  - [[Paper]](http:\u002F\u002Fproceedings.mlr.press\u002Fv37\u002Fbeygelzimer15.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002FVowpalWabbit\u002Fvowpal_wabbit\u002Fblob\u002Fmaster\u002Fvowpalwabbit\u002Fboosting.cc)\n\n- **Rademacher Observations, Private Data, and Boosting (ICML 2015)**\n  - Richard Nock, Giorgio Patrini, Arik Friedman\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1502.02322)\n\n- **Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions (ICML 2015)**\n  - Taehoon Lee, Sungroh Yoon\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fd0ad\u002Fbeef3053e98dd88ff74f42744417bc65a729.pdf)\n\n- **A Direct Boosting Approach for Semi-supervised Classification (IJCAI 2015)**\n  - Shaodan Zhai, Tian Xia, Zhongliang Li, Shaojun Wang\n  - [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F15\u002FPapers\u002F565.pdf)\n\n- **A Boosting Algorithm for Item Recommendation with Implicit Feedback (IJCAI 2015)**\n  - Yong Liu, Peilin Zhao, Aixin Sun, Chunyan Miao\n  - [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F15\u002FPapers\u002F255.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Frecommenders)\n\n- **Training-Time Optimization of a Budgeted Booster (IJCAI 2015)**\n  - Yi Huang, Brian Powers, Lev Reyzin\n  - [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F15\u002FPapers\u002F504.pdf)\n\n- **Optimal Action Extraction for Random Forests and Boosted Trees (KDD 2015)**\n  - Zhicheng Cui, Wenlin Chen, Yujie He, Yixin Chen\n  - [[Paper]](https:\u002F\u002Fwww.cse.wustl.edu\u002F~ychen\u002Fpublic\u002FOAE.pdf)\n\n- **Online Gradient Boosting (NIPS 2015)**\n  - Alina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1506.04820)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fcrm416\u002Fonline_boosting)\n  \n- **BROOF: Exploiting Out-of-Bag Errors Boosting and Random Forests for Effective Automated Classification (SIGIR 2015)**\n  - Thiago Salles, Marcos André Gonçalves, Victor Rodrigues, Leonardo C. da Rocha\n  - [[Paper]](https:\u002F\u002Fhomepages.dcc.ufmg.br\u002F~tsalles\u002Fbroof\u002Fappendix.pdf)\n\n- **Boosting Search with Deep Understanding of Contents and Users (WSDM 2015)**\n  - Kaihua Zhu\n  - [[Paper]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F282482189_Boosting_Search_with_Deep_Understanding_of_Contents_and_Users)\n\n## 2014\n- **On Boosting Sparse Parities (AAAI 2014)**\n  - Lev Reyzin\n  - [[Paper]](https:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI14\u002Fpaper\u002Fview\u002F8587)\n\n- **Joint Coupled-Feature Representation and Coupled Boosting for AD Diagnosis (CVPR 2014)**\n  - Yinghuan Shi, Heung-Il Suk, Yang Gao, Dinggang Shen\n  - [[Paper]](https:\u002F\u002Fwww.cv-foundation.org\u002Fopenaccess\u002Fcontent_cvpr_2014\u002Fpapers\u002FShi_Joint_Coupled-Feature_Representation_2014_CVPR_paper.pdf)\n\n- **From Categories to Individuals in Real Time - A Unified Boosting Approach (CVPR 2014)**\n  - David Hall, Pietro Perona\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6909424)\n  - [[Code]](http:\u002F\u002Fwww.vision.caltech.edu\u002F~dhall\u002Fprojects\u002FCategoriesToIndividuals\u002F)\n\n- **Efficient Boosted Exemplar-Based Face Detection (CVPR 2014)**\n  - Haoxiang Li, Zhe Lin, Jonathan Brandt, Xiaohui Shen, Gang Hua\n  - [[Paper]](http:\u002F\u002Fusers.eecs.northwestern.edu\u002F~xsh835\u002Fassets\u002Fcvpr14_exemplarfacedetection.pdf)\n\n- **Facial Expression Recognition via a Boosted Deep Belief Network (CVPR 2014)**\n  - Ping Liu, Shizhong Han, Zibo Meng, Yan Tong\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F6909629)\n\n- **Confidence-Rated Multiple Instance Boosting for Object Detection (CVPR 2014)**\n  - Karim Ali, Kate Saenko\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6909708)\n\n- **The Return of AdaBoost.MH: Multi-Class Hamming Trees (ICLR 2014)**\n  - Balázs Kégl\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1312.6086.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Faciditeam\u002Facidano\u002Fblob\u002Fmaster\u002Facidano\u002Futils\u002Fcost.py)\n\n- **Deep Boosting (ICML 2014)**\n  - Corinna Cortes, Mehryar Mohri, Umar Syed\n  - [[Paper]](http:\u002F\u002Fproceedings.mlr.press\u002Fv32\u002Fcortesb14.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fdeepboost)\n\n- **A Convergence Rate Analysis for LogitBoost, MART and Their Variant (ICML 2014)**\n  - Peng Sun, Tong Zhang, Jie Zhou\n  - [[Paper]](http:\u002F\u002Fproceedings.mlr.press\u002Fv32\u002Fsunc14.pdf)\n\n- **Boosting with Online Binary Learners for the Multiclass Bandit Problem (ICML 2014)**\n  - Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu\n  - [[Paper]](https:\u002F\u002Fwww.cc.gatech.edu\u002F~schen351\u002Fpaper\u002Ficml14boost.pdf)\n\n- **Boosting Multi-Step Autoregressive Forecasts (ICML 2014)**\n  - Souhaib Ben Taieb, Rob J. Hyndman\n  - [[Paper]](http:\u002F\u002Fproceedings.mlr.press\u002Fv32\u002Ftaieb14.pdf)\n\n- **Dynamic Programming Boosting for Discriminative Macro-Action Discovery (ICML 2014)**\n  - Leonidas Lefakis, François Fleuret\n  - [[Paper]](http:\u002F\u002Fproceedings.mlr.press\u002Fv32\u002Flefakis14.html)\n\n- **Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting (ICML 2014)**\n  - Oscar Beijbom, Mohammad J. Saberian, David J. Kriegman, Nuno Vasconcelos\n  - [[Paper]](http:\u002F\u002Fproceedings.mlr.press\u002Fv32\u002Fbeijbom14.pdf)\n\n- **A Multi-Class Boosting Method with Direct Optimization (KDD 2014)**\n  - Shaodan Zhai, Tian Xia, Shaojun Wang\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2623689)\n\n- **Gradient Boosted Feature Selection (KDD 2014)**\n  - Zhixiang Eddie Xu, Gao Huang, Kilian Q. Weinberger, Alice X. Zheng\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.04055)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fdmlc\u002Fxgboost)\n\n- **Multi-Class Deep Boosting (NIPS 2014)**\n  - Vitaly Kuznetsov, Mehryar Mohri, Umar Syed\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F5514-multi-class-deep-boosting)\n\n- **Deconvolution of High Dimensional Mixtures via Boosting with Application to Diffusion-Weighted MRI of Human Brain (NIPS 2014)**\n  - Charles Y. Zheng, Franco Pestilli, Ariel Rokem\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F5506-deconvolution-of-high-dimensional-mixtures-via-boosting-with-application-to-diffusion-weighted-mri-of-human-brain)\n\n- **A Drifting-Games Analysis for Online Learning and Applications to Boosting (NIPS 2014)**\n  - Haipeng Luo, Robert E. Schapire\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1406.1856)\n\n- **A Boosting Framework on Grounds of Online Learning (NIPS 2014)**\n  - Tofigh Naghibi Mohamadpoor, Beat Pfister\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F5512-a-boosting-framework-on-grounds-of-online-learning.pdf)\n  \n- **Gradient Boosting Factorization Machines (RECSYS 2014)**\n  - Chen Cheng, Fen Xia, Tong Zhang, Irwin King, Michael R. Lyu\n  - [[Paper]](http:\u002F\u002Ftongzhang-ml.org\u002Fpapers\u002Frecsys14-fm.pdf)\n\n## 2013\n\n- **Boosting Binary Keypoint Descriptors (CVPR 2013)**\n  - Tomasz Trzcinski, C. Mario Christoudias, Pascal Fua, Vincent Lepetit\n  - [[Paper]](https:\u002F\u002Fcvlab.epfl.ch\u002Fresearch\u002Fpage-90554-en-html\u002Fresearch-detect-binboost\u002F)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fbiotrump\u002Fcvlab-BINBOOST)\n\n- **PerturBoost: Practical Confidential Classifier Learning in the Cloud (ICDM 2013)**\n  - Keke Chen, Shumin Guo\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6729587)\n\n- **Multiclass Semi-Supervised Boosting Using Similarity Learning (ICDM 2013)**\n  - Jafar Tanha, Mohammad Javad Saberian, Maarten van Someren\n  - [[Paper]](https:\u002F\u002Fwww.cse.msu.edu\u002F~rongjin\u002Fpublications\u002FMultiClass-08.pdf)\n\n- **Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner (ICML 2013)**\n  - Peng Sun, Jie Zhou\n  - [[Paper]](http:\u002F\u002Fproceedings.mlr.press\u002Fv28\u002Fsun13.pdf)\n\n- **General Functional Matrix Factorization Using Gradient Boosting (ICML 2013)**\n  - Tianqi Chen, Hang Li, Qiang Yang, Yong Yu\n  - [[Paper]](http:\u002F\u002Fw.hangli-hl.com\u002Fuploads\u002F3\u002F1\u002F6\u002F8\u002F3168008\u002Ficml_2013.pdf)\n\n- **Margins, Shrinkage, and Boosting (ICML 2013)**\n  - Matus Telgarsky\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1303.4172)\n\n- **Quickly Boosting Decision Trees - Pruning Underachieving Features Early (ICML 2013)**\n  - Ron Appel, Thomas J. Fuchs, Piotr Dollár, Pietro Perona\n  - [[Paper]](http:\u002F\u002Fproceedings.mlr.press\u002Fv28\u002Fappel13.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fpdollar\u002Ftoolbox\u002Fblob\u002Fmaster\u002Fclassify\u002FadaBoostTrain.m)\n\n- **Human Boosting (ICML 2013)**\n  - Harsh H. Pareek, Pradeep Ravikumar\n  - [[Paper]](https:\u002F\u002Fwww.cs.cmu.edu\u002F~pradeepr\u002Fpaperz\u002Fhumanboosting.pdf)\n\n- **Collaborative Boosting for Activity Classification in Microblogs (KDD 2013)**\n  - Yangqiu Song, Zhengdong Lu, Cane Wing-ki Leung, Qiang Yang\n  - [[Paper]](http:\u002F\u002Fchbrown.github.io\u002Fkdd-2013-usb\u002Fkdd\u002Fp482.pdf)\n\n- **Direct 0-1 Loss Minimization and Margin Maximization with Boosting (NIPS 2013)**\n  - Shaodan Zhai, Tian Xia, Ming Tan, Shaojun Wang\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F5214-direct-0-1-loss-minimization-and-margin-maximization-with-boosting)\n\n- **Reservoir Boosting : Between Online and Offline Ensemble Learning (NIPS 2013)**\n  - Leonidas Lefakis, François Fleuret\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F5215-reservoir-boosting-between-online-and-offline-ensemble-learning)\n\n- **Non-Linear Domain Adaptation with Boosting (NIPS 2013)**\n  - Carlos J. Becker, C. Mario Christoudias, Pascal Fua\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F5200-non-linear-domain-adaptation-with-boosting)\n\n- **Boosting in the Presence of Label Noise (UAI 2013)**\n  - Jakramate Bootkrajang, Ata Kabán\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1309.6818)\n\n## 2012\n- **Contextual Boost for Pedestrian Detection (CVPR 2012)**\n  - Yuanyuan Ding, Jing Xiao\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.308.5611&rep=rep1&type=pdf)\n\n- **Shrink Boost for Selecting Multi-LBP Histogram Features in Object Detection (CVPR 2012)**\n  - Cher Keng Heng, Sumio Yokomitsu, Yuichi Matsumoto, Hajime Tamura\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6248061)\n\n- **Boosting Bottom-Up and Top-Down Visual Features for Saliency Estimation (CVPR 2012)**\n  - Ali Borji\n  - [[Paper]](http:\u002F\u002Filab.usc.edu\u002Fborji\u002Fpapers\u002Fcvpr-2012-BUModel-v4.pdf)\n\n- **Boosting Algorithms for Simultaneous Feature Extraction and Selection (CVPR 2012)**\n  - Mohammad J. Saberian, Nuno Vasconcelos\n  - [[Paper]](http:\u002F\u002Fsvcl.ucsd.edu\u002Fpublications\u002Fconference\u002F2012\u002Fcvpr\u002FSOPBoost.pdf)\n\n- **Sharing Features in Multi-class Boosting via Group Sparsity (CVPR 2012)**\n  - Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel\n  - [[Paper]](https:\u002F\u002Fcs.adelaide.edu.au\u002F~paulp\u002Fpublications\u002Fpubs\u002Fsharing_cvpr2012.pdf)\n\n- **Feature Weighting and Selection Using Hypothesis Margin of Boosting (ICDM 2012)**\n  - Malak Alshawabkeh, Javed A. Aslam, Jennifer G. Dy, David R. Kaeli\n  - [[Paper]](http:\u002F\u002Fwww.ece.neu.edu\u002Ffac-ece\u002Fjdy\u002Fpapers\u002Falshawabkeh-ICDM2012.pdf)\n\n- **An AdaBoost Algorithm for Multiclass Semi-supervised Learning (ICDM 2012)**\n  - Jafar Tanha, Maarten van Someren, Hamideh Afsarmanesh\n  - [[Paper]]https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6413799)\n  \n- **AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem (ICML 2012)**\n  - Peng Sun, Mark D. Reid, Jie Zhou\n  - [[Paper]](AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fpengsun\u002FAOSOLogitBoost)\n\n- **An Online Boosting Algorithm with Theoretical Justifications (ICML 2012)**\n  - Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1206.6422)\n\n- **Learning Image Descriptors with the Boosting-Trick (NIPS 2012)**\n  - Tomasz Trzcinski, C. Mario Christoudias, Vincent Lepetit, Pascal Fua\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4848-learning-image-descriptors-with-the-boosting-trick.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fbiotrump\u002Fcvlab-BINBOOST)\n\n- **Accelerated Training for Matrix-norm Regularization: A Boosting Approach (NIPS 2012)**\n  - Xinhua Zhang, Yaoliang Yu, Dale Schuurmans\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4663-accelerated-training-for-matrix-norm-regularization-a-boosting-approach)\n  \n- **Learning from Heterogeneous Sources via Gradient Boosting Consensus (SDM 2012)**\n  - Xiaoxiao Shi, Jean-François Paiement, David Grangier, Philip S. Yu\n  - [[Paper]](http:\u002F\u002Fdavid.grangier.info\u002Fpapers\u002F2012\u002Fshi_sdm_2012.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002FPriyeshV\u002FGBDT-CC)\n\n## 2011\n- **Selective Transfer Between Learning Tasks Using Task-Based Boosting (AAAI 2011)**\n  - Eric Eaton, Marie desJardins\n  - [[Paper]](http:\u002F\u002Fwww.cis.upenn.edu\u002F~eeaton\u002Fpapers\u002FEaton2011Selective.pdf)\n\n- **Incorporating Boosted Regression Trees into Ecological Latent Variable Models (AAAI 2011)**\n  - Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Dietterich\n  - [[Paper]](https:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI11\u002Fpaper\u002FviewFile\u002F3711\u002F4086)\n\n- **FlowBoost - Appearance Learning from Sparsely Annotated Video (CVPR 2011)**\n  - Karim Ali, David Hasler, François Fleuret\n  - [[Paper]](http:\u002F\u002Fwww.karimali.org\u002Fpublications\u002FAHF_CVPR11.pdf)\n\n- **AdaBoost on Low-Rank PSD Matrices for Metric Learning (CVPR 2011)**\n  - Jinbo Bi, Dijia Wu, Le Lu, Meizhu Liu, Yimo Tao, Matthias Wolf\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=5995363)\n\n- **Boosted Local Structured HOG-LBP for Object Localization (CVPR 2011)**\n  - Junge Zhang, Kaiqi Huang, Yinan Yu, Tieniu Tan\n  - [[Paper]](http:\u002F\u002Fwww.cbsr.ia.ac.cn\u002Fusers\u002Fynyu\u002F1682.pdf)\n\n- **A Direct Formulation for Totally-Corrective Multi-Class Boosting (CVPR 2011)**\n  - Chunhua Shen, Zhihui Hao\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?arnumber=5995554)\n\n- **Gated Classifiers: Boosting Under High Intra-class Variation (CVPR 2011)**\n  - Oscar M. Danielsson, Babak Rasolzadeh, Stefan Carlsson\n  - [[Paper]](http:\u002F\u002Fwww.nada.kth.se\u002Fcvap\u002Fcvg\u002Fpapers\u002FdanielssonCVPR11.pdf)\n\n- **TaylorBoost: First and Second-order Boosting Algorithms with Explicit Margin Control (CVPR 2011)**\n  - Mohammad J. Saberian, Hamed Masnadi-Shirazi, Nuno Vasconcelos\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5995605)\n  - [[Code]](https:\u002F\u002Fpythonhosted.org\u002Fbob.learn.boosting\u002F)\n\n- **Robust and Efficient Regularized Boosting Using Total Bregman Divergence (CVPR 2011)**\n  - Meizhu Liu, Baba C. Vemuri\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5995686)\n\n- **Treat Samples differently: Object Tracking with Semi-Supervised Online CovBoost (ICCV 2011)**\n  - Guorong Li, Lei Qin, Qingming Huang, Junbiao Pang, Shuqiang Jiang\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6126297)\n\n- **LinkBoost: A Novel Cost-Sensitive Boosting Framework for Community-Level Network Link Prediction (ICDM 2011)**\n  - Prakash Mandayam Comar, Pang-Ning Tan, Anil K. Jain\n  - [[Paper]](http:\u002F\u002Fwww.cse.msu.edu\u002F~ptan\u002Fpapers\u002Ficdm2011.pdf)\n\n- **Learning Markov Logic Networks via Functional Gradient Boosting (ICDM 2011)**\n  - Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik\n  - [[Paper]](https:\u002F\u002Fgithub.com\u002Fstarling-lab\u002FBoostSRL)\n  - [[Code]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6137236)\n\n- **Boosting on a Budget: Sampling for Feature-Efficient Prediction (ICML 2011)**\n  - Lev Reyzin\n  - [[Paper]](http:\u002F\u002Fwww.icml-2011.org\u002Fpapers\u002F348_icmlpaper.pdf)\n\n- **Multiclass Boosting with Hinge Loss based on Output Coding (ICML 2011)**\n  - Tianshi Gao, Daphne Koller\n  - [[Paper]](http:\u002F\u002Fai.stanford.edu\u002F~tianshig\u002Fpapers\u002FmulticlassHingeBoost-ICML2011.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fmemect\u002Fhao\u002Fblob\u002Fmaster\u002Fawesome\u002Fmulticlass-boosting.md)\n\n- **Generalized Boosting Algorithms for Convex Optimization (ICML 2011)**\n  - Alexander Grubb, Drew Bagnell\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1105.2054.pdf)\n\n- **Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach (IJCAI 2011)**\n  - Sriraam Natarajan, Saket Joshi, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik\n  - [[Paper]](http:\u002F\u002Fftp.cs.wisc.edu\u002Fmachine-learning\u002Fshavlik-group\u002Fnatarajan.ijcai11.pdf)\n\n- **Boosting with Maximum Adaptive Sampling (NIPS 2011)**\n  - Charles Dubout, François Fleuret\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4310-boosting-with-maximum-adaptive-sampling)\n\n- **The Fast Convergence of Boosting (NIPS 2011)**\n  - Matus Telgarsky\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4343-the-fast-convergence-of-boosting)\n\n- **ShareBoost: Efficient Multiclass Learning with Feature Sharing (NIPS 2011)**\n  - Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4213-shareboost-efficient-multiclass-learning-with-feature-sharing)\n\n- **Multiclass Boosting: Theory and Algorithms (NIPS 2011)**\n  - Mohammad J. Saberian, Nuno Vasconcelos\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4450-multiclass-boosting-theory-and-algorithms.pdf)\n\n- **Variance Penalizing AdaBoost (NIPS 2011)**\n  - Pannagadatta K. Shivaswamy, Tony Jebara\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4207-variance-penalizing-adaboost.pdf)\n\n- **MKBoost: A Framework of Multiple Kernel Boosting (SDM 2011)**\n  - Hao Xia, Steven C. H. Hoi\n  - [[Paper]](https:\u002F\u002Fink.library.smu.edu.sg\u002Fcgi\u002Fviewcontent.cgi?article=3280&context=sis_research)\n\n- **A Boosting Approach to Improving Pseudo-Relevance Feedback (SIGIR 2011)**\n  - Yuanhua Lv, ChengXiang Zhai, Wan Chen\n  - [[Paper]](http:\u002F\u002Fwww.tyr.unlu.edu.ar\u002FtallerIR\u002F2012\u002Fpapers\u002Fpseudorelevance.pdf)\n\n- **Bagging Gradient-Boosted Trees for High Precision, Low Variance Ranking Models (SIGIR 2011)**\n  - Yasser Ganjisaffar, Rich Caruana, Cristina Videira Lopes\n  - [[Paper]](http:\u002F\u002Fwww.ccs.neu.edu\u002Fhome\u002Fvip\u002Fteach\u002FMLcourse\u002F4_boosting\u002Fmaterials\u002Fbagging_lmbamart_jforests.pdf)\n\n- **Boosting as a Product of Experts (UAI 2011)**\n  - Narayanan Unny Edakunni, Gary Brown, Tim Kovacs\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1202.3716)\n\n- **Parallel Boosted Regression Trees for Web Search Ranking (WWW 2011)**\n  - Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal, Jennifer Paykin\n  - [[Paper]](http:\u002F\u002Fwww.cs.cornell.edu\u002F~kilian\u002Fpapers\u002Ffr819-tyreeA.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002FYS-L\u002Fpgbm)\n\n## 2010\n- **The Boosting Effect of Exploratory Behaviors (AAAI 2010)**\n  - Jivko Sinapov, Alexander Stoytchev\n  - [[Paper]](https:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI10\u002Fpaper\u002Fdownload\u002F1777\u002F2265)\n\n- **Boosting-Based System Combination for Machine Translation (ACL 2010)**\n  - Tong Xiao, Jingbo Zhu, Muhua Zhu, Huizhen Wang\n  - [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP10-1076)\n\n- **BagBoo: A Scalable Hybrid Bagging-the-Boosting Model (CIKM 2010)**\n  - Dmitry Yurievich Pavlov, Alexey Gorodilov, Cliff A. Brunk\n  - [[Paper]](http:\u002F\u002Fcache-default03h.cdn.yandex.net\u002Fdownload.yandex.ru\u002Fcompany\u002Fa_scalable_hybrid_bagging_the_boosting_model.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Farogozhnikov\u002Finfiniteboost)\n\n- **Automatic Detection of Craters in Planetary Images: an Embedded Framework Using Feature Selection and Boosting (CIKM 2010)**\n  - Wei Ding, Tomasz F. Stepinski, Lourenço P. C. Bandeira, Ricardo Vilalta, Youxi Wu, Zhenyu Lu, Tianyu Cao\n  - [[Paper]](https:\u002F\u002Fwww.uh.edu\u002F~rvilalta\u002Fpapers\u002F2010\u002Fcikm10.pdf)\n\n- **Facial Point Detection Using Boosted Regression and Graph Models (CVPR 2010)**\n  - Michel François Valstar, Brais Martínez, Xavier Binefa, Maja Pantic\n  - [[Paper]](https:\u002F\u002Fibug.doc.ic.ac.uk\u002Fmedia\u002Fuploads\u002Fdocuments\u002FCVPR-2010-ValstarEtAl-CAMERA.pdf)\n\n- **Boosting for Transfer Learning with Multiple Sources (CVPR 2010)**\n  - Yi Yao, Gianfranco Doretto\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5539857)\n\n- **Efficient Rotation Invariant Object Detection Using Boosted Random Ferns (CVPR 2010)**\n  - Michael Villamizar, Francesc Moreno-Noguer, Juan Andrade-Cetto, Alberto Sanfeliu\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.307.4002&rep=rep1&type=pdf)\n  \n- **Implicit Hierarchical Boosting for Multi-view Object Detection (CVPR 2010)**\n  - Xavier Perrotton, Marc Sturzel, Michel Roux\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5540115)\n\n- **On-Line Semi-Supervised Multiple-Instance Boosting (CVPR 2010)**\n  - Bernhard Zeisl, Christian Leistner, Amir Saffari, Horst Bischof\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5539860)\n\n- **Online Multi-Class LPBoost (CVPR 2010)**\n  - Amir Saffari, Martin Godec, Thomas Pock, Christian Leistner, Horst Bischof\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.165.8939&rep=rep1&type=pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Famirsaffari\u002Fonline-multiclass-lpboost)\n\n- **Homotopy Regularization for Boosting (ICDM 2010)**\n  - Zheng Wang, Yangqiu Song, Changshui Zhang\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5694094)\n\n- **Exploiting Local Data Uncertainty to Boost Global Outlier Detection (ICDM 2010)**\n  - Bo Liu, Jie Yin, Yanshan Xiao, Longbing Cao, Philip S. Yu\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5693984)\n\n- **Boosting Classifiers with Tightened L0-Relaxation Penalties (ICML 2010)**\n  - Noam Goldberg, Jonathan Eckstein\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F11df\u002Faed4ec2a2f72878789fa3a54d588d693bdda.pdf)\n\n- **Boosting for Regression Transfer (ICML 2010)**\n  - David Pardoe, Peter Stone\n  - [[Paper]](https:\u002F\u002Fwww.cs.utexas.edu\u002F~dpardoe\u002Fpapers\u002FICML10.pdf)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fjay15summer\u002FTwo-stage-TrAdaboost.R2)\n\n- **Boosted Backpropagation Learning for Training Deep Modular Networks (ICML 2010)**\n  - Alexander Grubb, J. Andrew Bagnell\n  - [[Paper]](https:\u002F\u002Ficml.cc\u002FConferences\u002F2010\u002Fpapers\u002F451.pdf)\n\n- **Fast Boosting Using Adversarial Bandits (ICML 2010)**\n  - Róbert Busa-Fekete, Balázs Kégl\n  - [[Paper]](https:\u002F\u002Fwww.lri.fr\u002F~kegl\u002Fresearch\u002FPDFs\u002FBuKe10.pdf)\n\n- **Boosting with Structure Information in the Functional Space: an Application to Graph Classification (KDD 2010)**\n  - Hongliang Fei, Jun Huan\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=1835804.1835886)\n\n- **Multi-task Learning for Boosting with Application to Web Search Ranking (KDD 2010)**\n  - Olivier Chapelle, Pannagadatta K. Shivaswamy, Srinivas Vadrevu, Kilian Q. Weinberger, Ya Zhang, Belle L. Tseng\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=1835953)\n\n- **A Theory of Multiclass Boosting (NIPS 2010)**\n  - Indraneel Mukherjee, Robert E. Schapire\n  - [[Paper]](http:\u002F\u002Frob.schapire.net\u002Fpapers\u002Fmultiboost-journal.pdf)\n\n- **Boosting Classifier Cascades (NIPS 2010)**\n  - Mohammad J. Saberian, Nuno Vasconcelos\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4033-boosting-classifier-cascades.pdf)\n\n- **Joint Cascade Optimization Using A Product Of Boosted Classifiers (NIPS 2010)**\n  - Leonidas Lefakis, François Fleuret\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4148-joint-cascade-optimization-using-a-product-of-boosted-classifiers)\n\n- **Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost (UAI 2010)**\n  - Ping Li\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1203.3491)\n  - [[Code]](https:\u002F\u002Fgithub.com\u002Fpengsun\u002FAOSOLogitBoost)\n\n## 2009\n\n- **Feature Selection for Ranking Using Boosted Trees (CIKM 2009)**\n  - Feng Pan, Tim Converse, David Ahn, Franco Salvetti, Gianluca Donato\n  - [[Paper]](http:\u002F\u002Fwww.francosalvetti.com\u002Fcikm09_camera2.pdf)\n\n- **Boosting KNN Text Classification Accuracy by Using Supervised Term Weighting Schemes (CIKM 2009)**\n  - Iyad Batal, Milos Hauskrecht\n  - [[Paper]](https:\u002F\u002Fpeople.cs.pitt.edu\u002F~milos\u002Fresearch\u002FCIKM_2009_boosting_KNN.pdf)\n  \n- **Stochastic Gradient Boosted Distributed Decision Trees (CIKM 2009)**\n  - Jerry Ye, Jyh-Herng Chow, Jiang Chen, Zhaohui Zheng\n  - [[Paper]](http:\u002F\u002Fcse.iitrpr.ac.in\u002Fckn\u002Fcourses\u002Ff2012\u002Fthomas.pdf)\n\n- **A General Magnitude-Preserving Boosting Algorithm for Search Ranking (CIKM 2009)**\n  - Chenguang Zhu, Weizhu Chen, Zeyuan Allen Zhu, Gang Wang, Dong Wang, Zheng Chen\n  - [[Paper]](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fwp-content\u002Fuploads\u002F2016\u002F06\u002Fcikm2009-1.pdf)\n  \n- **Reducing Joint Boost-Based Multiclass Classification to Proximity Search (CVPR 2009)**\n  - Alexandra Stefan, Vassilis Athitsos, Quan Yuan, Stan Sclaroff\n  - [[Paper]](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FReducing-JointBoost-based-multiclass-classification-Stefan-Athitsos\u002F08ba1a7d91ce9b4ac26869bfe4bb7c955b0d1a24)\n\n- **Imbalanced RankBoost for Efficiently Ranking Large-Scale Image-Video Collections (CVPR 2009)**\n  - Michele Merler, Rong Yan, John R. Smith\n  - [[Paper]](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FImbalanced-RankBoost-for-efficiently-ranking-Merler-Yan\u002F031ba6bf0d6df8bd3aa686ce85791b7d74f0b6d5)\n\n- **Regularized Multi-Class Semi-Supervised Boosting (CVPR 2009)**\n  - Amir Saffari, Christian Leistner, Horst Bischof\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F5206715)\n\n- **Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene (CVPR 2009)**\n  - Yuan Li, Chang Huang, Ram Nevatia\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.309.8335&rep=rep1&type=pdf)\n\n- **Boosted Multi-task Learning for Face Verification with Applications to Web Image and Video Search (CVPR 2009)**\n  - Xiaogang Wang, Cha Zhang, Zhengyou Zhang\n  - [[Paper]](http:\u002F\u002Fwww.ee.cuhk.edu.hk\u002F~xgwang\u002Fwebface.pdf)\n\n- **LidarBoost: Depth Superresolution for ToF 3D Shape Scanning (CVPR 2009)**\n  - Sebastian Schuon, Christian Theobalt, James E. Davis, Sebastian Thrun\n  - [[Paper]](http:\u002F\u002Fai.stanford.edu\u002F~schuon\u002Fsr\u002Fcvpr09_poster_lidarboost.pdf)\n\n- **Model Adaptation via Model Interpolation and Boosting for Web Search Ranking (EMNLP 2009)**\n  - Jianfeng Gao, Qiang Wu, Chris Burges, Krysta Marie Svore, Yi Su, Nazan Khan, Shalin Shah, Hongyan Zhou\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F7a82\u002F66335d0b44596574588eabb090bfeae4ab35.pdf)\n\n- **Finding Shareable Informative Patterns and Optimal Coding Matrix for Multiclass Boosting (ICCV 2009)**\n  - Bang Zhang, Getian Ye, Yang Wang, Jie Xu, Gunawan Herman\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5459146)\n\n- **RankBoost with L1 Regularization for Facial Expression Recognition and Intensity Estimation (ICCV 2009)**\n  - Peng Yang, Qingshan Liu, Dimitris N. Metaxas\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5459371)\n\n- **A Robust Boosting Tracker with Minimum Error Bound in a Co-Training Framework (ICCV 2009)**\n  - Rong Liu, Jian Cheng, Hanqing Lu\n  - [[Paper]](http:\u002F\u002Fnlpr-web.ia.ac.cn\u002F2009papers\u002Fgjhy\u002Fgh1.pdf)\n\n- **Tutorial Summary: Survey of Boosting from an Optimization Perspective (ICML 2009)**\n  - Manfred K. Warmuth, S. V. N. Vishwanathan\n  - [[Paper]](http:\u002F\u002Fwww.stat.purdue.edu\u002F~vishy\u002Ferlpboost\u002Fmanfred.pdf)\n\n- **Boosting Products of Base Classifiers (ICML 2009)**\n  - Balázs Kégl, Róbert Busa-Fekete\n  - [[Paper]](https:\u002F\u002Fusers.lal.in2p3.fr\u002Fkegl\u002Fresearch\u002FPDFs\u002FkeglBusafekete09.pdf)\n\n- **ABC-boost: Adaptive Base Class Boost for Multi-Class Classification (ICML 2009)**\n  - Ping Li\n  - [[Paper]](https:\u002F\u002Ficml.cc\u002FConferences\u002F2009\u002Fpapers\u002F417.pdf)\n\n- **Boosting with Structural Sparsity (ICML 2009)**\n  - John C. Duchi, Yoram Singer\n  - [[Paper]](https:\u002F\u002Fweb.stanford.edu\u002F~jduchi\u002Fprojects\u002FDuchiSi09a.pdf)\n\n- **Boosting Constrained Mutual Subspace Method for Robust Image-Set Based Object Recognition (IJCAI 2009)**\n  - Xi Li, Kazuhiro Fukui, Nanning Zheng\n  - [[Paper]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F220812439_Boosting_Constrained_Mutual_Subspace_Method_for_Robust_Image-Set_Based_Object_Recognition)\n\n- **Information Theoretic Regularization for Semi-supervised Boosting (KDD 2009)**\n  - Lei Zheng, Shaojun Wang, Yan Liu, Chi-Hoon Lee\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F5255\u002F242d50851ce56354e10ae8fdcee6f47591c9.pdf)\n\n- **Potential-Based Agnostic Boosting (NIPS 2009)**\n  - Adam Kalai, Varun Kanade\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3676-potential-based-agnostic-boosting)\n\n- **Positive Semidefinite Metric Learning with Boosting (NIPS 2009)**\n  - Chunhua Shen, Junae Kim, Lei Wang, Anton van den Hengel\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3658-positive-semidefinite-metric-learning-with-boosting)\n\n- **Boosting with Spatial Regularization (NIPS 2009)**\n  - Zhen James Xiang, Yongxin Taylor Xi, Uri Hasson, Peter J. Ramadge\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3696-boosting-with-spatial-regularization)\n  \n- **Effective Boosting of Na%C3%AFve Bayesian Classifiers by Local Accuracy Estimation (PAKDD 2009)**\n  - Zhipeng Xie\n  - [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-642-01307-2_88)\n\n- **Multi-resolution Boosting for Classification and Regression Problems (PAKDD 2009)**\n  - Chandan K. Reddy, Jin Hyeong Park\n  - [[Paper]](http:\u002F\u002Fdmkd.cs.vt.edu\u002Fpapers\u002FPAKDD09.pdf)\n\n- **Efficient Active Learning with Boosting (SDM 2009)**\n  - Zheng Wang, Yangqiu Song, Changshui Zhang\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fc8be\u002Fb70c37e4b4c4ad77e46b39060c977779d201.pdf)\n\n## 2008\n- **Group-Based Learning: A Boosting Approach (CIKM 2008)**\n  - Weijian Ni, Jun Xu, Hang Li, Yalou Huang\n  - [[Paper]](http:\u002F\u002Fwww.bigdatalab.ac.cn\u002F~junxu\u002Fpublications\u002FCIKM2008_GroupLearn.pdf)\n\n- **Semi-Supervised Boosting Using Visual Similarity Learning (CVPR 2008)**\n  - Christian Leistner, Helmut Grabner, Horst Bischof\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.144.7914&rep=rep1&type=pdf)\n\n- **Mining Compositional Features for Boosting (CVPR 2008)**\n  - Junsong Yuan, Jiebo Luo, Ying Wu\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?arnumber=4587347)\n\n- **Boosted Deformable Model for Human Body Alignment (CVPR 2008)**\n  - Xiaoming Liu, Ting Yu, Thomas Sebastian, Peter H. Tu\n  - [[Paper]](https:\u002F\u002Fwww.cse.msu.edu\u002F~liuxm\u002Fpublication\u002FLiu_Yu_Sebastian_Tu_cvpr08.pdf)\n\n- **Discriminative Modeling by Boosting on Multilevel Aggregates (CVPR 2008)**\n  - Jason J. Corso\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.409.3166&rep=rep1&type=pdf)\n\n- **Face Alignment via Boosted Ranking Model (CVPR 2008)**\n  - Hao Wu, Xiaoming Liu, Gianfranco Doretto\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F4587753)\n  \n- **Boosting Adaptive Linear Weak Classifiers for Online Learning and Tracking (CVPR 2008)**\n  - Toufiq Parag, Fatih Porikli, Ahmed M. Elgammal\n  - [[Paper]](https:\u002F\u002Fwww.merl.com\u002Fpublications\u002Fdocs\u002FTR2008-065.pdf)\n\n- **Detection with Multi-Exit Asymmetric Boosting (CVPR 2008)**\n  - Minh-Tri Pham, V-D. D. Hoang, Tat-Jen Cham\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.330.6364&rep=rep1&type=pdf)\n\n- **Boosting Ordinal Features for Accurate and Fast Iris Recognition (CVPR 2008)**\n  - Zhaofeng He, Zhenan Sun, Tieniu Tan, Xianchao Qiu, Cheng Zhong, Wenbo Dong\n  - [[Paper]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F224323296_Boosting_ordinal_features_for_accurate_and_fast_iris_recognition)\n\n- **Adaptive and Compact Shape Descriptor by Progressive Feature Combination and Selection with Boosting (CVPR 2008)**\n  - Cheng Chen, Yueting Zhuang, Jun Xiao, Fei Wu\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F4587613)\n  \n- **Boosting Relational Sequence Alignments (ICDM 2008)**\n  - Andreas Karwath, Kristian Kersting, Niels Landwehr\n  - [[Paper]](https:\u002F\u002Fwww.cs.uni-potsdam.de\u002F~landwehr\u002FICDM08boosting.pdf)\n\n- **Boosting with Incomplete Information (ICML 2008)**\n  - Gholamreza Haffari, Yang Wang, Shaojun Wang, Greg Mori, Feng Jiao\n  - [[Paper]](http:\u002F\u002Fusers.monash.edu.au\u002F~gholamrh\u002Fpublications\u002Fboosting_icml08_slides.pdf)\n  \n- **ManifoldBoost: Stagewise Function Approximation for Fully-, Semi- and Un-supervised Learning (ICML 2008)**\n  - Nicolas Loeff, David A. Forsyth, Deepak Ramachandran\n  - [[Paper]](http:\u002F\u002Freason.cs.uiuc.edu\u002Fdeepak\u002Fmanifoldboost.pdf)\n\n- **Random Classification Noise Defeats All Convex Potential Boosters (ICML 2008)**\n  - Philip M. Long, Rocco A. Servedio\n  - [[Paper]](http:\u002F\u002Fphillong.info\u002Fpublications\u002FLS09_potential.pdf)\n\n- **Multi-class Cost-Sensitive Boosting with P-norm Loss Functions (KDD 2008)**\n  - Aurelie C. Lozano, Naoki Abe\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=1401953)\n\n- **MCBoost: Multiple Classifier Boosting for Perceptual Co-clustering of Images and Visual Features (NIPS 2008)**\n  - Tae-Kyun Kim, Roberto Cipolla\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3483-mcboost-multiple-classifier-boosting-for-perceptual-co-clustering-of-images-and-visual-features)\n\n- **PSDBoost: Matrix-Generation Linear Programming for Positive Semidefinite Matrices Learning (NIPS 2008)**\n  - Chunhua Shen, Alan Welsh, Lei Wang\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.879.7750&rep=rep1&type=pdf)\n\n- **On the Design of Loss Functions for Classification: Theory, Robustness to Outliers, and SavageBoost (NIPS 2008)**\n  - Hamed Masnadi-Shirazi, Nuno Vasconcelos\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3591-on-the-design-of-loss-functions-for-classification-theory-robustness-to-outliers-and-savageboost)\n\n- **Adaptive Martingale Boosting (NIPS 2008)**\n  - Philip M. Long, Rocco A. Servedio\n  - [[Paper]](http:\u002F\u002Fphillong.info\u002Fpublications\u002FLS08_adaptive_martingale_boosting.pdf)\n  \n- **A Boosting Algorithm for Learning Bipartite Ranking Functions with Partially Labeled Data (SIGIR 2008)**\n  - Massih-Reza Amini, Tuong-Vinh Truong, Cyril Goutte\n  - [[Paper]](http:\u002F\u002Fama.liglab.fr\u002F~amini\u002FPublis\u002FSemiSupRanking_sigir08.pdf)\n\n## 2007\n\n- **Using Error-Correcting Output Codes with Model-Refinement to Boost Centroid Text Classifier (ACL 2007)**\n  - Songbo Tan\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=1557794)\n\n- **Fast Human Pose Estimation using Appearance and Motion via Multi-Dimensional Boosting Regression (CVPR 2007)**\n  - Alessandro Bissacco, Ming-Hsuan Yang, Stefano Soatto\n  - [[Paper]](http:\u002F\u002Fvision.ucla.edu\u002Fpapers\u002FbissaccoYS07.pdf)\n\n- **Generic Face Alignment using Boosted Appearance Model (CVPR 2007)**\n  - Xiaoming Liu\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?arnumber=4270290)\n\n- **Eigenboosting: Combining Discriminative and Generative Information (CVPR 2007)**\n  - Helmut Grabner, Peter M. Roth, Horst Bischof\n  - [[Paper]](https:\u002F\u002Fwww.tugraz.at\u002Ffileadmin\u002Fuser_upload\u002FInstitute\u002FICG\u002FDocuments\u002Flrs\u002Fpubs\u002Fgrabner_cvpr_07.pdf)\n\n- **Online Learning Asymmetric Boosted Classifiers for Object Detection (CVPR 2007)**\n  - Minh-Tri Pham, Tat-Jen Cham\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F4270108)\n\n- **Improving Part based Object Detection by Unsupervised Online Boosting (CVPR 2007)**\n  - Bo Wu, Ram Nevatia\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F4270173)\n\n- **A Specialized Processor Suitable for AdaBoost-Based Detection with Haar-like Features (CVPR 2007)**\n  - Masayuki Hiromoto, Kentaro Nakahara, Hiroki Sugano, Yukihiro Nakamura, Ryusuke Miyamoto\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F4270413)\n\n- **Simultaneous Object Detection and Segmentation by Boosting Local Shape Feature based Classifier (CVPR 2007)**\n  - Bo Wu, Ram Nevatia\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.309.9795&rep=rep1&type=pdf)\n\n- **Compositional Boosting for Computing Hierarchical Image Structures (CVPR 2007)**\n  - Tianfu Wu, Gui-Song Xia, Song Chun Zhu\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F4270059)\n\n- **Boosting Coded Dynamic Features for Facial Action Units and Facial Expression Recognition (CVPR 2007)**\n  - Peng Yang, Qingshan Liu, Dimitris N. Metaxas\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F4270084)\n\n- **Object Classification in Visual Surveillance Using Adaboost (CVPR 2007)**\n  - John-Paul Renno, Dimitrios Makris, Graeme A. Jones\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F4270512)\n\n- **A Boosting Regression Approach to Medical Anatomy Detection (CVPR 2007)**\n  - Shaohua Kevin Zhou, Jinghao Zhou, Dorin Comaniciu\n  - [[Paper]](http:\u002F\u002Fww.w.comaniciu.net\u002FPapers\u002FBoostingRegression_CVPR07.pdf)\n\n- **Joint Real-time Object Detection and Pose Estimation Using Probabilistic Boosting Network (CVPR 2007)**\n  - Jingdan Zhang, Shaohua Kevin Zhou, Leonard McMillan, Dorin Comaniciu\n  - [[Paper]](http:\u002F\u002Fcsbio.unc.edu\u002Fmcmillan\u002Fpubs\u002FCVPR07_Zhang.pdf)\n\n- **Kernel Sharing With Joint Boosting For Multi-Class Concept Detection (CVPR 2007)**\n  - Wei Jiang, Shih-Fu Chang, Alexander C. Loui\n  - [[Paper]](http:\u002F\u002Fwww.ee.columbia.edu\u002F~wjiang\u002Freferences\u002Fjiangcvprws07.pdf)\n\n- **Scale-Space Based Weak Regressors for Boosting (ECML 2007)**\n  - Jin Hyeong Park, Chandan K. Reddy\n  - [[Paper]](http:\u002F\u002Fwww.cs.wayne.edu\u002F~reddy\u002FPapers\u002FECML07.pdf)\n\n- **Avoiding Boosting Overfitting by Removing Confusing Samples (ECML 2007)**\n  - Alexander Vezhnevets, Olga Barinova\n  - [[Paper]](http:\u002F\u002Fgroups.inf.ed.ac.uk\u002Fcalvin\u002Fhp_avezhnev\u002FPubs\u002FAvoidingBoostingOverfitting.pdf)\n\n- **DynamicBoost: Boosting Time Series Generated by Dynamical Systems (ICCV 2007)**\n  - René Vidal, Paolo Favaro\n  - [[Paper]](http:\u002F\u002Fvision.jhu.edu\u002Fassets\u002FVidalICCV07.pdf)\n\n- **Incremental Learning of Boosted Face Detector (ICCV 2007)**\n  - Chang Huang, Haizhou Ai, Takayoshi Yamashita, Shihong Lao, Masato Kawade\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.126.9012&rep=rep1&type=pdf)\n\n- **Gradient Feature Selection for Online Boosting (ICCV 2007)**\n  - Xiaoming Liu, Ting Yu\n  - [[Paper]](https:\u002F\u002Fwww.cse.msu.edu\u002F~liuxm\u002Fpublication\u002FLiu_Yu_ICCV2007.pdf)\n\n- **Fast Training and Selection of Haar Features Using Statistics in Boosting-based Face Detection (ICCV 2007)**\n  - Minh-Tri Pham, Tat-Jen Cham\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.212.6173&rep=rep1&type=pdf)\n\n- **Cluster Boosted Tree Classifier for Multi-View - Multi-Pose Object Detection (ICCV 2007)**\n  - Bo Wu, Ramakant Nevatia\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.309.9885&rep=rep1&type=pdf)\n\n- **Asymmetric Boosting (ICML 2007)**\n  - Hamed Masnadi-Shirazi, Nuno Vasconcelos\n  - [[Paper]](http:\u002F\u002Fwww.svcl.ucsd.edu\u002Fpublications\u002Fconference\u002F2007\u002Ficml07\u002FAsymmetricBoosting.pdf)\n\n- **Boosting for Transfer Learning (ICML 2007)**\n  - Wenyuan Dai, Qiang Yang, Gui-Rong Xue, Yong Yu\n  - [[Paper]](http:\u002F\u002Fwww.cs.ust.hk\u002F~qyang\u002FDocs\u002F2007\u002Ftradaboost.pdf)\n  \n- **Gradient Boosting for Kernelized Output Spaces (ICML 2007)**\n  - Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.435.3970&rep=rep1&type=pdf)\n\n- **Boosting a Complete Technique to Find MSS and MUS Thanks to a Local Search Oracle (IJCAI 2007)**\n  - Éric Grégoire, Bertrand Mazure, Cédric Piette\n  - [[Paper]](http:\u002F\u002Fwww.cril.univ-artois.fr\u002F~piette\u002FIJCAI07_HYCAM.pdf)\n\n- **Training Conditional Random Fields Using Virtual Evidence Boosting (IJCAI 2007)**\n  - Lin Liao, Tanzeem Choudhury, Dieter Fox, Henry A. Kautz\n  - [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F07\u002FPapers\u002F407.pdf)\n\n- **Simple Training of Dependency Parsers via Structured Boosting (IJCAI 2007)**\n  - Qin Iris Wang, Dekang Lin, Dale Schuurmans\n  - [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F07\u002FPapers\u002F284.pdf)\n\n- **Real Boosting a la Carte with an Application to Boosting Oblique Decision Tree (IJCAI 2007)**\n  - Claudia Henry, Richard Nock, Frank Nielsen\n  - [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F07\u002FPapers\u002F135.pdf)\n\n- **Managing Domain Knowledge and Multiple Models with Boosting (IJCAI 2007)**\n  - Peng Zang, Charles Lee Isbell Jr.\n  - [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F07\u002FPapers\u002F185.pdf)\n\n- **Model-Shared Subspace Boosting for Multi-label Classification (KDD 2007)**\n  - Rong Yan, Jelena Tesic, John R. Smith\n  - [[Paper]](http:\u002F\u002Frogerioferis.com\u002FVisualRecognitionAndSearch2014\u002Fmaterial\u002Fpapers\u002FIMARSKDD2007.pdf)\n\n- **Regularized Boost for Semi-Supervised Learning (NIPS 2007)**\n  - Ke Chen, Shihai Wang\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3167-regularized-boost-for-semi-supervised-learning.pdf)\n\n- **Boosting Algorithms for Maximizing the Soft Margin (NIPS 2007)**\n  - Manfred K. Warmuth, Karen A. Glocer, Gunnar Rätsch\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3374-boosting-algorithms-for-maximizing-the-soft-margin.pdf)\n\n- **McRank: Learning to Rank Using Multiple Classification and Gradient Boosting (NIPS 2007)**\n  - Ping Li, Christopher J. C. Burges, Qiang Wu\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3270-mcrank-learning-to-rank-using-multiple-classification-and-gradient-boosting.pdf)\n\n- **One-Pass Boosting (NIPS 2007)**\n  - Zafer Barutçuoglu, Philip M. Long, Rocco A. Servedio\n  - [[Paper]](http:\u002F\u002Fphillong.info\u002Fpublications\u002FBLS07_one_pass.pdf)\n\n- **Boosting the Area under the ROC Curve (NIPS 2007)**\n  - Philip M. Long, Rocco A. Servedio\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3247-boosting-the-area-under-the-roc-curve.pdf)\n\n- **FilterBoost: Regression and Classification on Large Datasets (NIPS 2007)**\n  - Joseph K. Bradley, Robert E. Schapire\n  - [[Paper]](http:\u002F\u002Frob.schapire.net\u002Fpapers\u002FFilterBoost_paper.pdf)\n\n- **A General Boosting Method and its Application to Learning Ranking Functions for Web Search (NIPS 2007)**\n  - Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier Chapelle, Keke Chen, Gordon Sun\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F8f8d\u002F874a3f0217289ba317b1f6175ac3b6f73d70.pdf)\n\n- **Efficient Multiclass Boosting Classification with Active Learning (SDM 2007)**\n  - Jian Huang, Seyda Ertekin, Yang Song, Hongyuan Zha, C. Lee Giles\n  - [[Paper]](https:\u002F\u002Fepubs.siam.org\u002Fdoi\u002Fabs\u002F10.1137\u002F1.9781611972771.27)\n\n- **AdaRank: a Boosting Algorithm for Information Retrieval (SIGIR 2007)**\n  - Jun Xu, Hang Li\n  - [[Paper]](http:\u002F\u002Fwww.bigdatalab.ac.cn\u002F~junxu\u002Fpublications\u002FSIGIR2007_AdaRank.pdf)\n\n## 2006\n\n- **Gradient Boosting for Sequence Alignment (AAAI 2006)**\n  - Charles Parker, Alan Fern, Prasad Tadepalli\n  - [[Paper]](http:\u002F\u002Fweb.engr.oregonstate.edu\u002F~afern\u002Fpapers\u002Faaai06-align.pdf)\n\n- **Boosting Kernel Models for Regression (ICDM 2006)**\n  - Ping Sun, Xin Yao\n  - [[Paper]](https:\u002F\u002Fwww.cs.bham.ac.uk\u002F~xin\u002Fpapers\u002Ficdm06SunYao.pdf)\n\n- **Boosting for Learning Multiple Classes with Imbalanced Class Distribution (ICDM 2006)**\n  - Yanmin Sun, Mohamed S. Kamel, Yang Wang\n  - [[Paper]](http:\u002F\u002Fpeople.ee.duke.edu\u002F~lcarin\u002FImbalancedClassDistribution.pdf)\n\n- **Boosting the Feature Space: Text Classification for Unstructured Data on the Web (ICDM 2006)**\n  - Yang Song, Ding Zhou, Jian Huang, Isaac G. Councill, Hongyuan Zha, C. Lee Giles\n  - [[Paper]](http:\u002F\u002Fsonyis.me\u002Fpaperpdf\u002Ficdm06_song.pdf)\n\n- **Totally Corrective Boosting Algorithms that Maximize the Margin (ICML 2006)**\n  - Manfred K. Warmuth, Jun Liao, Gunnar Rätsch\n  - [[Paper]](https:\u002F\u002Fusers.soe.ucsc.edu\u002F~manfred\u002Fpubs\u002FC75.pdf)\n  \n- **How Boosting the Margin Can Also Boost Classifier Complexity (ICML 2006)**\n  - Lev Reyzin, Robert E. Schapire\n  - [[Paper]](http:\u002F\u002Frob.schapire.net\u002Fpapers\u002Fboost_complexity.pdf)\n\n- **Multiclass Boosting with Repartitioning (ICML 2006)**\n  - Ling Li\n  - [[Paper]](https:\u002F\u002Fauthors.library.caltech.edu\u002F72259\u002F1\u002Fp569-li.pdf)\n\n- **AdaBoost is Consistent (NIPS 2006)**\n  - Peter L. Bartlett, Mikhail Traskin\n  - [[Paper]](http:\u002F\u002Fjmlr.csail.mit.edu\u002Fpapers\u002Fvolume8\u002Fbartlett07b\u002Fbartlett07b.pdf)\n\n- **Boosting Structured Prediction for Imitation Learning (NIPS 2006)**\n  - Nathan D. Ratliff, David M. Bradley, J. Andrew Bagnell, Joel E. Chestnutt\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3154-boosting-structured-prediction-for-imitation-learning.pdf)\n\n- **Chained Boosting (NIPS 2006)**\n  - Christian R. Shelton, Wesley Huie, Kin Fai Kan\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2981-chained-boosting)\n  \n- **When Efficient Model Averaging Out-Performs Boosting and Bagging (PKDD 2006)**\n  - Ian Davidson, Wei Fan\n  - [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F11871637_46)\n\n## 2005\n- **Semantic Place Classification of Indoor Environments with Mobile Robots Using Boosting (AAAI 2005)**\n  - Axel Rottmann, Óscar Martínez Mozos, Cyrill Stachniss, Wolfram Burgard\n  - [[Paper]](http:\u002F\u002Fwww2.informatik.uni-freiburg.de\u002F~stachnis\u002Fpdf\u002Frottmann05aaai.pdf)\n  \n- **Boosting-based Parse Reranking with Subtree Features (ACL 2005)**\n  - Taku Kudo, Jun Suzuki, Hideki Isozaki\n  - [[Paper]](http:\u002F\u002Fchasen.org\u002F~taku\u002Fpublications\u002Facl2005.pdf)\n\n- **Using RankBoost to Compare Retrieval Systems (CIKM 2005)**\n  - Huyen-Trang Vu, Patrick Gallinari\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.98.9470&rep=rep1&type=pdf)\n\n- **Classifier Fusion Using Shared Sampling Distribution for Boosting (ICDM 2005)**\n  - Costin Barbu, Raja Tanveer Iqbal, Jing Peng\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F1565659)\n\n- **Semi-Supervised Mixture of Kernels via LPBoost Methods (ICDM 2005)**\n  - Jinbo Bi, Glenn Fung, Murat Dundar, R. Bharat Rao\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F1565728)\n\n- **Efficient Discriminative Learning of Bayesian Network Classifier via Boosted Augmented Naive Bayes (ICML 2005)**\n  - Yushi Jing, Vladimir Pavlovic, James M. Rehg\n  - [[Paper]](http:\u002F\u002Fmrl.isr.uc.pt\u002Fpub\u002Fbscw.cgi\u002Fd27355\u002FJing05Efficient.pdf)\n\n- **Unifying the Error-Correcting and Output-Code AdaBoost within the Margin Framework (ICML 2005)**\n  - Yijun Sun, Sinisa Todorovic, Jian Li, Dapeng Wu\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.138.4246&rep=rep1&type=pdf)\n\n- **A Smoothed Boosting Algorithm Using Probabilistic Output Codes (ICML 2005)**\n  - Rong Jin, Jian Zhang\n  - [[Paper]](http:\u002F\u002Fwww.stat.purdue.edu\u002F~jianzhan\u002Fpapers\u002Ficml05jin.pdf)\n\n- **Robust Boosting and its Relation to Bagging (KDD 2005)**\n  - Saharon Rosset\n  - [[Paper]](https:\u002F\u002Fwww.tau.ac.il\u002F~saharon\u002Fpapers\u002Fbagboost.pdf)\n\n- **Efficient Computations via Scalable Sparse Kernel Partial Least Squares and Boosted Latent Features (KDD 2005)**\n  - Michinari Momma\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.387.2078&rep=rep1&type=pdf)\n\n- **Multiple Instance Boosting for Object Detection (NIPS 2005)**\n  - Paul A. Viola, John C. Platt, Cha Zhang\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.138.8312&rep=rep1&type=pdf)\n\n- **Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations (NIPS 2005)**\n  - Aurelie C. Lozano, Sanjeev R. Kulkarni, Robert E. Schapire\n  - [[Paper]](https:\u002F\u002Fwww.cs.princeton.edu\u002F~schapire\u002Fpapers\u002Fbetamix.pdf)\n  \n- **Boosted decision trees for word recognition in handwritten document retrieval (SIGIR 2005)**\n  - Nicholas R. Howe, Toni M. Rath, R. Manmatha\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.152.1551&rep=rep1&type=pdf)\n  \n- **Obtaining Calibrated Probabilities from Boosting (UAI 2005)**\n  - Alexandru Niculescu-Mizil, Rich Caruana\n  - [[Paper]](https:\u002F\u002Fwww.cs.cornell.edu\u002F~caruana\u002Fniculescu.scldbst.crc.rev4.pdf)\n\n## 2004\n\n- **Online Parallel Boosting (AAAI 2004)**\n  - Jesse A. Reichler, Harlan D. Harris, Michael A. Savchenko\n  - [[Paper]](https:\u002F\u002Fwww.aaai.org\u002FPapers\u002FAAAI\u002F2004\u002FAAAI04-059.pdf)\n\n- **A Boosting Approach to Multiple Instance Learning (ECML 2004)**\n  - Peter Auer, Ronald Ortner\n  - [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-540-30115-8_9)\n\n- **A Boosting Algorithm for Classification of Semi-Structured Text (EMNLP 2004)**\n  - Taku Kudo, Yuji Matsumoto\n  - [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FW04-3239)\n\n- **Text Classification by Boosting Weak Learners based on Terms and Concepts (ICDM 2004)**\n  - Stephan Bloehdorn, Andreas Hotho\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F1410303)\n\n- **Boosting Grammatical Inference with Confidence Oracles (ICML 2004)**\n  - Jean-Christophe Janodet, Richard Nock, Marc Sebban, Henri-Maxime Suchier\n  - [[Paper]](http:\u002F\u002Fwww1.univ-ag.fr\u002F~rnock\u002FArticles\u002FDrafts\u002Ficml04-jnss.pdf)\n\n- **Surrogate Maximization\u002FMinimization Algorithms for AdaBoost and the Logistic Regression Model (ICML 2004)**\n  - Zhihua Zhang, James T. Kwok, Dit-Yan Yeung\n  - [[Paper]](https:\u002F\u002Ficml.cc\u002FConferences\u002F2004\u002Fproceedings\u002Fpapers\u002F77.pdf)\n\n- **Training Conditional Random Fields via Gradient Tree Boosting (ICML 2004)**\n  - Thomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov\n  - [[Paper]](http:\u002F\u002Fweb.engr.oregonstate.edu\u002F~tgd\u002Fpublications\u002Fml2004-treecrf.pdf)\n\n- **Boosting Margin Based Distance Functions for Clustering (ICML 2004)**\n  - Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall\n  - [[Paper]](http:\u002F\u002Fwww.cs.huji.ac.il\u002F~daphna\u002Fpapers\u002Fdistboost-icml.pdf)\n\n- **Column-Generation Boosting Methods for Mixture of Kernels (KDD 2004)**\n  - Jinbo Bi, Tong Zhang, Kristin P. Bennett\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.94.6359&rep=rep1&type=pdf)\n\n- **Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging (NIPS 2004)**\n  - Vladimir Koltchinskii, Manel Martínez-Ramón, Stefan Posse\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2699-optimal-aggregation-of-classifiers-and-boosting-maps-in-functional-magnetic-resonance-imaging.pdf)\n\n- **Boosting on Manifolds: Adaptive Regularization of Base Classifiers (NIPS 2004)**\n  - Balázs Kégl, Ligen Wang\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2613-boosting-on-manifolds-adaptive-regularization-of-base-classifiers)\n\n- **Contextual Models for Object Detection Using Boosted Random Fields (NIPS 2004)**\n  - Antonio Torralba, Kevin P. Murphy, William T. Freeman\n  - [[Paper]](https:\u002F\u002Fwww.cs.ubc.ca\u002F~murphyk\u002FPapers\u002FBRF-nips04-camera.pdf)\n\n- **Generalization Error and Algorithmic Convergence of Median Boosting (NIPS 2004)**\n  - Balázs Kégl\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.70.8990&rep=rep1&type=pdf)\n\n- **An Application of Boosting to Graph Classification (NIPS 2004)**\n  - Taku Kudo, Eisaku Maeda, Yuji Matsumoto\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2739-an-application-of-boosting-to-graph-classification)\n\n- **Logistic Regression and Boosting for Labeled Bags of Instances (PAKDD 2004)**\n  - Xin Xu, Eibe Frank\n  - [[Paper]](https:\u002F\u002Fwww.cs.waikato.ac.nz\u002F~ml\u002Fpublications\u002F2004\u002Fxu-frank.pdf)\n\n- **Fast and Light Boosting for Adaptive Mining of Data Streams (PAKDD 2004)**\n  - Fang Chu, Carlo Zaniolo\n  - [[Paper]](http:\u002F\u002Fweb.cs.ucla.edu\u002F~zaniolo\u002Fpapers\u002FNBCAJMW77MW0J8CP.pdf)\n\n## 2003\n- **On Boosting and the Exponential Loss (AISTATS 2003)**\n  - Abraham J. Wyner\n  - [[Paper]](http:\u002F\u002Fwww-stat.wharton.upenn.edu\u002F~ajw\u002Fexploss.ps)\n\n- **Boosting Support Vector Machines for Text Classification through Parameter-Free Threshold Relaxation (CIKM 2003)**\n  - James G. Shanahan, Norbert Roma\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=956911)\n\n- **Learning Cross-Document Structural Relationships Using Boosting (CIKM 2003)**\n  - Zhu Zhang, Jahna Otterbacher, Dragomir R. Radev\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.128.7712&rep=rep1&type=pdf)\n  \n- **On Boosting Improvement: Error Reduction and Convergence Speed-Up (ECML 2003)**\n  - Marc Sebban, Henri-Maxime Suchier\n  - [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-540-39857-8_32)\n\n- **Boosting Lazy Decision Trees (ICML 2003)**\n  - Xiaoli Zhang Fern, Carla E. Brodley\n  - [[Paper]](https:\u002F\u002Fwww.aaai.org\u002FPapers\u002FICML\u002F2003\u002FICML03-026.pdf)\n\n- **On the Convergence of Boosting Procedures (ICML 2003)**\n  - Tong Zhang, Bin Yu\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fdd3f\u002F901b232280533fbdb9e57f144f44723617cf.pdf)\n\n- **Linear Programming Boosting for Uneven Datasets (ICML 2003)**\n  - Jure Leskovec, John Shawe-Taylor\n  - [[Paper]](https:\u002F\u002Fcs.stanford.edu\u002Fpeople\u002Fjure\u002Fpubs\u002Ftextbooster-icml03.pdf)\n\n- **Monte Carlo Theory as an Explanation of Bagging and Boosting (IJCAI 2003)**\n  - Roberto Esposito, Lorenza Saitta\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=1630733)\n\n- **On the Dynamics of Boosting (NIPS 2003)**\n  - Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2535-on-the-dynamics-of-boosting)\n\n- **Mutual Boosting for Contextual Inference (NIPS 2003)**\n  - Michael Fink, Pietro Perona\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2520-mutual-boosting-for-contextual-inference)\n\n- **Boosting Versus Covering (NIPS 2003)**\n  - Kohei Hatano, Manfred K. Warmuth\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2532-boosting-versus-covering)\n\n- **Multiple-Instance Learning via Disjunctive Programming Boosting (NIPS 2003)**\n  - Stuart Andrews, Thomas Hofmann\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2478-multiple-instance-learning-via-disjunctive-programming-boosting)\n\n- **Averaged Boosting: A Noise-Robust Ensemble Method (PAKDD 2003)**\n  - Yongdai Kim\n  - [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-36175-8_38)\n\n- **SMOTEBoost: Improving Prediction of the Minority Class in Boosting (PKDD 2003)**\n  - Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, Kevin W. Bowyer\n  - [[Paper]](https:\u002F\u002Fwww3.nd.edu\u002F~nchawla\u002Fpapers\u002FECML03.pdf)\n\n## 2002\n\n- **Minimum Majority Classification and Boosting (AAAI 2002)**\n  - Philip M. Long\n  - [[Paper]](http:\u002F\u002Fphillong.info\u002Fpublications\u002Fminmaj.pdf)\n\n- **Ranking Algorithms for Named Entity Extraction: Boosting and the Voted Perceptron (ACL 2002)**\n  - Michael Collins\n  - [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP02-1062)\n\n- **Boosting to Correct Inductive Bias in Text Classification (CIKM 2002)**\n  - Yan Liu, Yiming Yang, Jaime G. Carbonell\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=584792.584850)\n  \n- **How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code (ECML 2002)**\n  - Günther Eibl, Karl Peter Pfeiffer\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=650068)\n\n- **Scaling Boosting by Margin-Based Inclusionof Features and Relations (ECML 2002)**\n  - Susanne Hoche, Stefan Wrobel\n  - [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-36755-1_13)\n\n- **A Robust Boosting Algorithm (ECML 2002)**\n  - Richard Nock, Patrice Lefaucheur\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=650081)\n\n- **iBoost: Boosting Using an instance-Based Exponential Weighting Scheme (ECML 2002)**\n  - Stephen Kwek, Chau Nguyen\n  - [[Paper]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F220516082_iBoost_Boosting_using_an_instance-based_exponential_weighting_scheme)\n\n- **Boosting Density Function Estimators (ECML 2002)**\n  - Franck Thollard, Marc Sebban, Philippe Ézéquel\n  - [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F3-540-36755-1_36)\n  \n- **Statistical Behavior and Consistency of Support Vector Machines, Boosting, and Beyond (ICML 2002)**\n  - Tong Zhang\n  - [[Paper]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F221344927_Statistical_Behavior_and_Consistency_of_Support_Vector_Machines_Boosting_and_Beyond)\n\n- **A Boosted Maximum Entropy Model for Learning Text Chunking (ICML 2002)**\n  - Seong-Bae Park, Byoung-Tak Zhang\n  - [[Paper]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F221345636_A_Boosted_Maximum_Entropy_Model_for_Learning_Text_Chunking)\n\n- **Towards Large Margin Speech Recognizers by Boosting and Discriminative Training (ICML 2002)**\n  - Carsten Meyer, Peter Beyerlein\n  - [[Paper]](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTowards-Large-Margin-Speech-Recognizers-by-Boosting-Meyer-Beyerlein\u002F8408479e36da812cdbf6bc15f7849c3e76a1016d)\n\n- **Incorporating Prior Knowledge into Boosting (ICML 2002)**\n  - Robert E. Schapire, Marie Rochery, Mazin G. Rahim, Narendra K. Gupta\n  - [[Paper]](http:\u002F\u002Frob.schapire.net\u002Fpapers\u002Fboostknowledge.pdf)\n\n- **Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation (ICML 2002)**\n  - Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, János A. Csirik\n  - [[Paper]](http:\u002F\u002Fwww.cs.utexas.edu\u002F~ai-lab\u002Fpubs\u002FICML02-tac.pdf)\n\n- **MARK: A Boosting Algorithm for Heterogeneous Kernel Models (KDD 2002)**\n  - Kristin P. Bennett, Michinari Momma, Mark J. Embrechts\n  - [[Paper]](http:\u002F\u002Fhomepages.rpiscrews.us\u002F~bennek\u002Fpapers\u002Fkdd2.pdf)\n\n- **Predicting rare classes: can boosting make any weak learner strong (KDD 2002)**\n  - Mahesh V. Joshi, Ramesh C. Agarwal, Vipin Kumar\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.13.1159&rep=rep1&type=pdf)\n\n- **Kernel Design Using Boosting (NIPS 2002)**\n  - Koby Crammer, Joseph Keshet, Yoram Singer\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fff79\u002F344807e972fdd7e5e1c3ed5c539dd1aeecbe.pdf)\n\n- **FloatBoost Learning for Classification (NIPS 2002)**\n  - Stan Z. Li, ZhenQiu Zhang, Heung-Yeung Shum, HongJiang Zhang\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F8ccc\u002F5ef87eab96a4cae226750eba8322b30606ea.pdf)\n\n- **Discriminative Learning for Label Sequences via Boosting (NIPS 2002)**\n  - Yasemin Altun, Thomas Hofmann, Mark Johnson\n  - [[Paper]](http:\u002F\u002Fweb.science.mq.edu.au\u002F~mjohnson\u002Fpapers\u002Fnips02.pdf)\n\n- **Boosting Density Estimation (NIPS 2002)**\n  - Saharon Rosset, Eran Segal\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2298-boosting-density-estimation.pdf)\n\n- **Self Supervised Boosting (NIPS 2002)**\n  - Max Welling, Richard S. Zemel, Geoffrey E. Hinton\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F6a2a\u002Ff112a803e70c23b7055de2e73007cf42c301.pdf)\n\n- **Boosted Dyadic Kernel Discriminants (NIPS 2002)**\n  - Baback Moghaddam, Gregory Shakhnarovich\n  - [[Paper]](http:\u002F\u002Fwww.merl.com\u002Fpublications\u002Fdocs\u002FTR2002-55.pdf)\n  \n- **A Method to Boost Support Vector Machines (PAKDD 2002)**\n  - Lili Diao, Keyun Hu, Yuchang Lu, Chunyi Shi\n  - [[Paper]](https:\u002F\u002Felkingarcia.github.io\u002FPapers\u002FMLDM07.pdf)\n\n- **A Method to Boost Naive Bayesian Classifiers (PAKDD 2002)**\n  - Lili Diao, Keyun Hu, Yuchang Lu, Chunyi Shi\n  - [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-47887-6_11)\n\n- **Predicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting (PKDD 2002)**\n  - Mahesh V. Joshi, Ramesh C. Agarwal, Vipin Kumar\n  - [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-45681-3_20)\n\n- **Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance (PKDD 2002)**\n  - Yuta Choki, Einoshin Suzuki\n  - [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-45681-3_8)\n\n- **Staged Mixture Modelling and Boosting (UAI 2002)**\n  - Christopher Meek, Bo Thiesson, David Heckerman\n  - [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1301.0586)\n\n- **Advances in Boosting (UAI 2002)**\n  - Robert E. Schapire\n  - [[Paper]](http:\u002F\u002Frob.schapire.net\u002Fpapers\u002Fuai02.pdf)\n\n## 2001\n- **Is Regularization Unnecessary for Boosting? (AISTATS 2001)**\n  - Wenxin Jiang\n  - [[Paper]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F2439718_Is_Regularization_Unnecessary_for_Boosting)\n\n- **Online Bagging and Boosting (AISTATS 2001)**\n  - Nikunj C. Oza, Stuart J. Russell\n  - [[Paper]](https:\u002F\u002Fti.arc.nasa.gov\u002Fm\u002Fprofile\u002Foza\u002Ffiles\u002Fozru01a.pdf)\n  \n- **Text Categorization Using Transductive Boosting (ECML 2001)**\n  - Hirotoshi Taira, Masahiko Haruno\n  - [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-44795-4_39)\n\n- **Improving Term Extraction by System Combination Using Boosting (ECML 2001)**\n  - Jordi Vivaldi, Lluís Màrquez, Horacio Rodríguez\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3108351)\n\n- **Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example (ECML 2001)**\n  - Günther Eibl, Karl Peter Pfeiffer\n  - [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-44795-4_10)\n\n- **On the Practice of Branching Program Boosting (ECML 2001)**\n  - Tapio Elomaa, Matti Kääriäinen\n  - [[Paper]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F221112522_On_the_Practice_of_Branching_Program_Boosting)\n\n- **Boosting Mixture Models for Semi-supervised Learning (ICANN 2001)**\n  - Yves Grandvalet, Florence d'Alché-Buc, Christophe Ambroise\n  - [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-44668-0_7\n\n- **A Comparison of Stacking with Meta Decision Trees to Bagging, Boosting, and Stacking with other Methods (ICDM 2001)**\n  - Bernard Zenko, Ljupco Todorovski, Saso Dzeroski\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.23.3118&rep=rep1&type=pdf)\n\n- **Using Boosting to Simplify Classification Models (ICDM 2001)**\n  - Virginia Wheway\n  - [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F989565)\n\n- **Evaluating Boosting Algorithms to Classify Rare Classes: Comparison and Improvements (ICDM 2001)**\n  - Mahesh V. Joshi, Vipin Kumar, Ramesh C. Agarwal\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fb829\u002Ffe743e4beeeed65d32d2d7931354df7a2f60.pdf)\n  - [[Code]]( )\n\n- **Boosting Neighborhood-Based Classifiers (ICML 2001)**\n  - Marc Sebban, Richard Nock, Stéphane Lallich\n  - [[Paper]](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FBoosting-Neighborhood-Based-Classifiers-Sebban-Nock\u002Fee88e3bbe8a7e81cae7ee53da2c824de7c82f882)\n\n- **Boosting Noisy Data (ICML 2001)**\n  - Abba Krieger, Chuan Long, Abraham J. Wyner\n  - [[Paper]](https:\u002F\u002Fwww.researchgate.net\u002Fprofile\u002FAbba_Krieger\u002Fpublication\u002F221345435_Boosting_Noisy_Data\u002Flinks\u002F00463528a1ba641692000000.pdf)\n\n- **Some Theoretical Aspects of Boosting in the Presence of Noisy Data (ICML 2001)**\n  - Wenxin Jiang\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload;jsessionid=2494A2C06ACA22FA971AC1C29B53FF62?doi=10.1.1.27.7231&rep=rep1&type=pdf)\n\n- **Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection (ICML 2001)**\n  - Sanmay Das\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F93b6\u002F25a0e35b59fa6a3e7dc1cbdb31268d62d69f.pdf)\n\n- **The Distributed Boosting Algorithm (KDD 2001)**\n  - Aleksandar Lazarevic, Zoran Obradovic\n  - [[Paper]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F2488971_The_Distributed_Boosting_Algorithm)\n\n- **Experimental Comparisons of Online and Batch Versions of Bagging and Boosting (KDD 2001)**\n  - Nikunj C. Oza, Stuart J. Russell\n  - [[Paper]](https:\u002F\u002Fpeople.eecs.berkeley.edu\u002F~russell\u002Fpapers\u002Fkdd01-online.pdf)\n\n- **Semi-supervised MarginBoost (NIPS 2001)**\n  - Florence d'Alché-Buc, Yves Grandvalet, Christophe Ambroise\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F2197\u002Ff1c2d55827b6928cc80030922569acce2d6c.pdf)\n\n- **Boosting and Maximum Likelihood for Exponential Models (NIPS 2001)**\n  - Guy Lebanon, John D. Lafferty\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2042-boosting-and-maximum-likelihood-for-exponential-models.pdf)\n\n- **Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade (NIPS 2001)**\n  - Paul A. Viola, Michael J. Jones\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.68.4306&rep=rep1&type=pdf)\n  \n- **Boosting Localized Classifiers in Heterogeneous Databases (SDM 2001)**\n  - Aleksandar Lazarevic, Zoran Obradovic\n  - [[Paper]](https:\u002F\u002Fepubs.siam.org\u002Fdoi\u002Fabs\u002F10.1137\u002F1.9781611972719.14)\n\n- **Greedy function approximation: A gradient boosting machine (Ann. Statist 2001)**\n  - Jerome H. Friedman\n  - [[Paper]](https:\u002F\u002Fprojecteuclid.org\u002Fjournals\u002Fannals-of-statistics\u002Fvolume-29\u002Fissue-5\u002FGreedy-function-approximation-A-gradient-boosting-machine\u002F10.1214\u002Faos\u002F1013203451.full)\n\n## 2000\n- **Boosted Wrapper Induction (AAAI 2000)**\n  - Dayne Freitag, Nicholas Kushmerick\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fd009\u002Fa2bd48a9d1971fbc0d99f6df00539a62048a.pdf)\n\n- **An Improved Boosting Algorithm and its Application to Text Categorization (CIKM 2000)**\n  - Fabrizio Sebastiani, Alessandro Sperduti, Nicola Valdambrini\n  - [[Paper]](http:\u002F\u002Fnmis.isti.cnr.it\u002Fsebastiani\u002FPublications\u002FCIKM00.pdf)\n\n- **Boosting for Document Routing (CIKM 2000)**\n  - Raj D. Iyer, David D. Lewis, Robert E. Schapire, Yoram Singer, Amit Singhal\n  - [[Paper]](http:\u002F\u002Fsinghal.info\u002Fcikm-2000.pdf)\n\n- **On the Boosting Pruning Problem (ECML 2000)**\n  - Christino Tamon, Jie Xiang\n  - [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-45164-1_41)\n\n- **Boosting Applied to Word Sense Disambiguation (ECML 2000)**\n  - Gerard Escudero, Lluís Màrquez, German Rigau\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=649539)\n\n- **An Empirical Study of MetaCost Using Boosting Algorithms (ECML 2000)**\n  - Kai Ming Ting\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.218.1624&rep=rep1&type=pdf)\n\n- **FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness (ICML 2000)**\n  - Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum\n  - [[Paper]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F221345746_FeatureBoost_A_Meta-Learning_Algorithm_that_Improves_Model_Robustness)\n\n- **Comparing the Minimum Description Length Principle and Boosting in the Automatic Analysis of Discourse (ICML 2000)**\n  - Tadashi Nomoto, Yuji Matsumoto\n  - [[Paper]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F221344998_Comparing_the_Minimum_Description_Length_Principle_and_Boosting_in_the_Automatic_Analysis_of_Discourse)\n\n- **A Boosting Approach to Topic Spotting on Subdialogues (ICML 2000)**\n  - Kary Myers, Michael J. Kearns, Satinder P. Singh, Marilyn A. Walker\n  - [[Paper]](https:\u002F\u002Fwww.cis.upenn.edu\u002F~mkearns\u002Fpapers\u002Ftopicspot.pdf)\n\n- **A Comparative Study of Cost-Sensitive Boosting Algorithms (ICML 2000)**\n  - Kai Ming Ting\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=657944)\n\n- **Boosting a Positive-Data-Only Learner (ICML 2000)**\n  - Andrew R. Mitchell\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fsummary?doi=10.1.1.34.3669)\n\n- **A Column Generation Algorithm For Boosting (ICML 2000)**\n  - Kristin P. Bennett, Ayhan Demiriz, John Shawe-Taylor\n  - [[Paper]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload;jsessionid=1828D5853F656BD6892E9C2C446ECC68?doi=10.1.1.16.9612&rep=rep1&type=pdf)\n\n- **A Gradient-Based Boosting Algorithm for Regression Problems (NIPS 2000)**\n  - Richard S. Zemel, Toniann Pitassi\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fc41a\u002F9417f5605b55bdd216d119e47669a92f5c50.pdf)\n\n- **Weak Learners and Improved Rates of Convergence in Boosting (NIPS 2000)**\n  - Shie Mannor, Ron Meir\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F1906-weak-learners-and-improved-rates-of-convergence-in-boosting.pdf)\n\n- **Adaptive Boosting for Spatial Functions with Unstable Driving Attributes (PAKDD 2000)**\n  - Aleksandar Lazarevic, Tim Fiez, Zoran Obradovic\n  - [[Paper]](http:\u002F\u002Fwww.dabi.temple.edu\u002F~zoran\u002Fpapers\u002Flazarevic01j.pdf)\n\n- **Scaling Up a Boosting-Based Learner via Adaptive Sampling (PAKDD 2000)**\n  - Carlos Domingo, Osamu Watanabe\n  - [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-45571-X_37)\n\n- **Learning First Order Logic Time Series Classifiers: Rules and Boosting (PKDD 2000)**\n  - Juan J. Rodríguez Diez, Carlos Alonso González, Henrik Boström\n  - [[Paper]](https:\u002F\u002Fpeople.dsv.su.se\u002F~henke\u002Fpapers\u002Frodriguez00b.pdf)\n\n- **Bagging and Boosting with Dynamic Integration of Classifiers (PKDD 2000)**\n  - Alexey Tsymbal, Seppo Puuronen\n  - [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-45372-5_12)\n\n- **Text Filtering by Boosting Naive Bayes Classifiers (SIGIR 2000)**\n  - Yu-Hwan Kim, Shang-Yoon Hahn, Byoung-Tak Zhang\n  - [[Paper]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F221299823_Text_filtering_by_boosting_Naive_Bayes_classifiers)\n\n## 1999\n- **Boosting Methodology for Regression Problems (AISTATS 1999)**\n  - Greg Ridgeway, David Madigan, Thomas Richardson\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F5f19\u002F6a8baa281b2190c4519305bec8f5c91c8e5a.pdf)\n\n- **Boosting Applied to Tagging and PP Attachment (EMNLP 1999)**\n  - Steven Abney, Robert E. Schapire, Yoram Singer\n  - [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FW99-0606)\n\n- **Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees (ICML 1999)**\n  - Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F067e\u002F86836ddbcb5e2844e955c16e058366a18c77.pdf)\n\n- **AdaCost: Misclassification Cost-Sensitive Boosting (ICML 1999)**\n  - Wei Fan, Salvatore J. Stolfo, Junxin Zhang, Philip K. Chan\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F9ddf\u002Fbc2cc5c1b13b80a1a487b9caa57e80edd863.pdf)\n\n- **Boosting a Strong Learner: Evidence Against the Minimum Margin (ICML 1999)**\n  - Michael Bonnell Harries\n  - [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=657480)\n\n- **Boosting Algorithms as Gradient Descent (NIPS 1999)**\n  - Llew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F1766-boosting-algorithms-as-gradient-descent.pdf)\n\n- **Boosting with Multi-Way Branching in Decision Trees (NIPS 1999)**\n  - Yishay Mansour, David A. McAllester\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F1659-boosting-with-multi-way-branching-in-decision-trees.pdf)\n\n- **Potential Boosters (NIPS 1999)**\n  - Nigel Duffy, David P. Helmbold\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F4884\u002Fc765b6ceab7bdfb6703489810c8a386fd2a8.pdf)\n\n## 1998\n- **An Efficient Boosting Algorithm for Combining Preferences (ICML 1998)**\n  - Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer\n  - [[Paper]](http:\u002F\u002Fjmlr.csail.mit.edu\u002Fpapers\u002Fvolume4\u002Ffreund03a\u002Ffreund03a.pdf)\n\n- **Query Learning Strategies Using Boosting and Bagging (ICML 1998)**\n  - Naoki Abe, Hiroshi Mamitsuka\n  - [[Paper]](https:\u002F\u002Fwww.bic.kyoto-u.ac.jp\u002Fpathway\u002Fmami\u002Fpubs\u002FFiles\u002Ficml98.pdf)\n\n- **Regularizing AdaBoost (NIPS 1998)**\n  - Gunnar Rätsch, Takashi Onoda, Klaus-Robert Müller\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F0afc\u002F9de245547c675d40ad29240e2788c0416f91.pdf)\n\n## 1997\n- **Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods (ICML 1997)**\n  - Robert E. Schapire, Yoav Freund, Peter Barlett, Wee Sun Lee\n  - [[Paper]](https:\u002F\u002Fwww.cc.gatech.edu\u002F~isbell\u002Ftutorials\u002Fboostingmargins.pdf)\n\n- **Using Output Codes to Boost Multiclass Learning Problems (ICML 1997)**\n  - Robert E. Schapire\n  - [[Paper]](http:\u002F\u002Frob.schapire.net\u002Fpapers\u002FSchapire97.pdf)\n\n- **Improving Regressors Using Boosting Techniques (ICML 1997)**\n  - Harris Drucker\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F8d49\u002Fe2dedb817f2c3330e74b63c5fc86d2399ce3.pdf)\n\n- **Pruning Adaptive Boosting (ICML 1997)**\n  - Dragos D. Margineantu, Thomas G. Dietterich\n  - [[Paper]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fb25f\u002F615fc139fbdeccc3bcf4462f908d7f8e37f9.pdf)\n\n- **Training Methods for Adaptive Boosting of Neural Networks (NIPS 1997)**\n  - Holger Schwenk, Yoshua Bengio\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F1335-training-methods-for-adaptive-boosting-of-neural-networks.pdf)\n\n## 1996\n- **Experiments with a New Boosting Algorithm (ICML 1996)**\n  - Yoav Freund, Robert E. Schapire\n  - [[Paper]](https:\u002F\u002Fcseweb.ucsd.edu\u002F~yfreund\u002Fpapers\u002Fboostingexperiments.pdf)\n\n## 1995\n- **Boosting Decision Trees (NIPS 1995)**\n  - Harris Drucker, Corinna Cortes\n  - [[Paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F1059-boosting-decision-trees.pdf)\n\n## 1994\n- **Boosting and Other Machine Learning Algorithms (ICML 1994)**\n  - Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik\n  - [[Paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FB9781558603356500155)\n\n--------------------------------------------------------------------------------\n\n**License**\n\n- [CC0 Universal](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-gradient-boosting-papers\u002Fblob\u002Fmaster\u002FLICENSE)\n","# 令人惊叹的梯度提升研究论文\n[![Awesome](https:\u002F\u002Fcdn.rawgit.com\u002Fsindresorhus\u002Fawesome\u002Fd7305f38d29fed78fa85652e3a63e154dd8e8829\u002Fmedia\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome) [![欢迎提交PR](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-welcome-brightgreen.svg?style=flat-square)](http:\u002F\u002Fmakeapullrequest.com) ![许可证](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fbenedekrozemberczki\u002Fawesome-gradient-boosting-papers.svg?color=blue) [![仓库大小](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Frepo-size\u002Fbenedekrozemberczki\u002Fawesome-gradient-boosting-papers.svg)](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-gradient-boosting-papers\u002Farchive\u002Fmaster.zip) [![benedekrozemberczki](https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002Fbenrozemberczki?style=social&logo=twitter)](https:\u002F\u002Ftwitter.com\u002Fintent\u002Ffollow?screen_name=benrozemberczki)\n\u003Cp align=\"center\">\n  \u003Cimg width=\"450\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fbenedekrozemberczki_awesome-gradient-boosting-papers_readme_06d507204868.gif\">\n\u003C\u002Fp>\n\n--------------------------------\n\n一份精心整理的梯度提升与自适应提升相关论文列表，包含来自以下会议的实现：\n\n- 机器学习\n   * [NeurIPS](https:\u002F\u002Fnips.cc\u002F) \n   * [ICML](https:\u002F\u002Ficml.cc\u002F) \n   * [ICLR](https:\u002F\u002Ficlr.cc\u002F)\n- 计算机视觉\n   * [CVPR](http:\u002F\u002Fcvpr2019.thecvf.com\u002F)\n   * [ICCV](http:\u002F\u002Ficcv2019.thecvf.com\u002F)\n   * [ECCV](https:\u002F\u002Feccv2018.org\u002F)\n- 自然语言处理\n   * [ACL](http:\u002F\u002Fwww.acl2019.org\u002FEN\u002Findex.xhtml)\n   * [NAACL](https:\u002F\u002Fnaacl2019.org\u002F)\n   * [EMNLP](https:\u002F\u002Fwww.emnlp-ijcnlp2019.org\u002F) \n- 数据科学\n   * [KDD](https:\u002F\u002Fwww.kdd.org\u002F)\n   * [CIKM](http:\u002F\u002Fwww.cikmconference.org\u002F)   \n   * [ICDM](http:\u002F\u002Ficdm2019.bigke.org\u002F)\n   * [SDM](https:\u002F\u002Fwww.siam.org\u002FConferences\u002FCM\u002FConference\u002Fsdm19)   \n   * [PAKDD](http:\u002F\u002Fpakdd2019.medmeeting.org)\n   * [PKDD\u002FECML](http:\u002F\u002Fecmlpkdd2019.org)\n   * [RECSYS](https:\u002F\u002Frecsys.acm.org\u002F)\n   * [SIGIR](https:\u002F\u002Fsigir.org\u002F)\n   * [WWW](https:\u002F\u002Fwww2019.thewebconf.org\u002F)\n   * [WSDM](www.wsdm-conference.org) \n- 人工智能\n   * [AAAI](https:\u002F\u002Fwww.aaai.org\u002F)\n   * [AISTATS](https:\u002F\u002Fwww.aistats.org\u002F)\n   * [ICANN](https:\u002F\u002Fe-nns.org\u002Ficann2019\u002F)   \n   * [IJCAI](https:\u002F\u002Fwww.ijcai.org\u002F)\n   * [UAI](http:\u002F\u002Fwww.auai.org\u002F)\n\n类似的集合还包括关于[图分类](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-graph-classification)、[分类\u002F回归树](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-decision-tree-papers)、[欺诈检测](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-fraud-detection-papers)、[蒙特卡洛树搜索](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-monte-carlo-tree-search-papers)以及[社区发现](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-community-detection)的论文及其实现。\n\n\n## 2025年\n\n- **森林中的免费午餐：提升树集成的功能等价剪枝（AAAI 2025）**\n  - Youssouf Emine, Alexandre Forel, Idriss Malek, Thibaut Vidal\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2408.16167)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Feminyous\u002Ffipe)\n\n- **基于梯度提升的监督分数建模（AAAI 2025）**\n  - Changyuan Zhao, Hongyang Du, Guangyuan Liu, Dusit Niyato\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2411.01159)\n\n- **加法模型提升：新见解与病理现象（AISTATS 2025）**\n  - Rickmer Schulte, David Rügamer\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2503.05538)\n\n- **FairRegBoost：用于公平且可扩展回归的端到端数据处理框架（CIKM 2025）**\n  - Nico Lässig, Melanie Herschel\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3746252.3761277)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002FNicoLaessig\u002Ffairregboost)\n\n- **用于金融欺诈检测的联邦梯度提升：银行业实证研究（CIKM 2025）**\n  - Dae-Young Park, In-Young Ko, Taek-Ho Lee, Junghye Lee\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fepdf\u002F10.1145\u002F3746252.3760891)\n\n- **用于带回归和分类的区间删失数据的提升方法（ICLR 2025）**\n  - Yuan Bian, Grace Y. Yi, Wenqing He\n  - [[论文]](https:\u002F\u002Fopenreview.net\u002Fforum?id=DzbUL4AJPP)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fkrisyuanbian\u002FL2BOOST-IC)\n\n- **NRGBoost：基于能量的生成式提升树（ICLR 2025）**\n  - João Bravo\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2410.03535)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fajoo\u002Fnrgboost)\n\n- **梯度提升强化学习（ICML 2025）**\n  - Benjamin Fuhrer, Chen Tessler, Gal Dalal\n  - [[论文]](https:\u002F\u002Fgithub.com\u002FNVlabs\u002Fgbrl)\n  - [[代码]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2407.08250)\n\n- **提升树中特征贡献的快速计算（UAI 2025）**\n  - Zhongli Jiang, Min Zhang, Dabao Zhang\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2407.03515)\n\n- **用于回归任务的鲁棒XGBoost集成学习（UAI 2025）**\n  - Atri Vivek Sharma, Panagiotis Kouvaros, Alessio Lomuscio\n  - [[论文]](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Fsharma25a.html)\n\n## 2024年\n\n- **正交梯度提升用于更简单的加法规则集成（AISTATS 2024）**\n  - Fan Yang, Pierre Le Bodic, Michael Kamp, Mario Boley\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.15691)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Ffyan102\u002FFCOGB)\n\n- **分布式提升：一种基于数据蒸馏的增强方法（CIKM 2024）**\n  - Xuechao Chen, Wenchao Meng, Peiran Wang, Qihang Zhou\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3627673.3679897)\n\n- **通过提升进行对抗性模仿学习（ICLR 2024）**\n  - Jonathan D. Chang, Dhruv Sreenivas, Yingbing Huang, Kianté Brantley, Wen Sun\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2404.08513)\n\n- **迭代弱可学习性与多分类AdaBoost（KDD 2024）**\n  - In-Koo Cho, Jonathan A. Libgober, Cheng Ding\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671842)\n\n- **基于梯度提升的提升建模（KDD 2024）**\n  - Bulat Ibragimov, Anton Vakhrushev\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fepdf\u002F10.1145\u002F3637528.3672019)\n\n- **AdaGMLP：AdaBoosting GNN到MLP的知识蒸馏（KDD 2024）**\n  - Weigang Lu, Ziyu Guan, Wei Zhao, Yaming Yang\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2405.14307)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002FWeigangLu\u002FAdaGMLP-KDD24)\n\n- **PEMBOT：帕累托集成的多任务提升树（KDD 2024）**\n  - Gokul Swamy, Anoop Saladi, Arunita Das, Shobhit Niranjan\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fepdf\u002F10.1145\u002F3637528.3671619)\n\n## 2023年\n\n- **为提升树计算溯因解释（AISTATS 2023）**\n  - 吉勒·奥德马尔、让-玛丽·拉涅兹、皮埃尔·马基斯、尼古拉斯·什切潘斯基\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.07740)\n\n- **基于策略外的提升学习（AISTATS 2023）**\n  - 本·伦敦、莱维·卢、泰德·桑德勒、托尔斯滕·约阿希姆斯\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.01148)\n\n- **变分提升软树（AISTATS 2023）**\n  - 特里斯坦·辛坎、塔莫·鲁卡特、菲利普·施密特、马丁·维斯图巴、阿图尔·贝卡索夫\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.10706)\n\n- **克雷洛夫-贝尔曼提升：一般状态空间中的超线性策略评估（AISTATS 2023）**\n  - 埃里克·夏、马丁·J·韦恩赖特\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.11377)\n\n- **FairGBM：具有公平性约束的梯度提升（ICLR 2023）**\n  - 安德烈·费雷拉·克鲁斯、卡塔琳娜·贝伦、若昂·布拉沃、佩德罗·萨莱罗、佩德罗·比扎罗\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.07850)\n\n- **梯度提升执行高斯过程推断（ICLR 2023）**\n  - 阿列克谢·乌斯季缅科、阿尔乔姆·别利亚科夫、柳德米拉·普罗霍连科娃\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.05608)\n\n\n## 2022年\n\n- **TransBoost：用于提升金融包容性的提升树核迁移学习算法（AAAI 2022）**\n  - 叶恒·孙、田璐、丛王、袁李、怀宇·傅、景然·董、云杰·徐\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.02365)\n\n- **一种弹性分布式提升算法（ICML 2022）**\n  - 尤瓦尔·菲尔穆斯、伊丹·梅哈莱尔、沙伊·莫兰\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.04713)\n\n- **快速且可证明鲁棒的决策树与提升（ICML 2022）**\n  - 郭俊奇、滕明卓、高伟、周志华\n  - [[论文]](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fguo22h.html)\n\n- **通过间隔提升构建鲁棒集成模型（ICML 2022）**\n  - 张丁淮、张洪洋、亚伦·C·库维尔、约书亚·本吉奥、普拉迪普·拉维库马尔、阿伦·赛·苏加拉\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.03362)\n\n- **基于检索的梯度提升决策树用于疾病风险评估（KDD 2022）**\n  - 马汉东、曹嘉航、方宇晨、张卫南、盛文博、张绍典、于勇\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3534678.3539052)\n\n- **联邦功能梯度提升（AISTATS 2022）**\n  - 申泽邦、哈迈德·哈萨尼、萨蒂延·卡莱、阿敏·卡尔巴西\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.06972)\n\n- **ExactBoost：在组合型和不可分解指标中直接提升间隔（AISTATS 2022）**\n  - 丹尼尔·西拉格、卡罗丽娜·皮亚扎、蒂亚戈·拉莫斯、若昂·维托尔·罗马诺、罗伯托·I·奥利维拉、保罗·奥伦斯坦\n  - [[论文]](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fcsillag22a.html)\n\n## 2021年\n\n- **基于精确度的提升（AAAI 2021）**\n  - 穆罕默德·侯赛因·尼克拉万、马尔詹·莫瓦赫丹、桑德拉·齐勒斯\n  - [[论文]](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17105)\n\n- **BNN：利用有限数据提升神经网络框架（CIKM 2021）**\n  - 阿米特·利夫内、罗伊·多尔、布拉查·沙皮拉、利奥尔·罗卡赫\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3459637.3482414)\n\n- **基于梯度提升树的无监督域适应静态恶意软件检测（CIKM 2021）**\n  - 齐盼盼、王伟、朱磊、吴锡强\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3459637.3482400)\n\n- **个体公平的梯度提升（ICLR 2021）**\n  - 亚历山大·瓦尔戈、张帆、米哈伊尔·尤罗奇金、岳凯·孙\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.16785)\n\n- **神经排序器是否仍被梯度提升决策树超越（ICLR 2021）**\n  - 秦振、严乐、庄宏磊、泰奕、帕苏马尔蒂·拉马·库马尔、王玄辉、迈克尔·本德斯基、马克·纳约克\n  - [[论文]](https:\u002F\u002Ficlr.cc\u002Fvirtual\u002F2021\u002Fspotlight\u002F3536)\n\n- **AdaGCN：将图卷积网络自适应提升为深度模型（ICLR 2021）**\n  - 孙科、朱占兴、林周臣\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.05081)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fdatake\u002FAdaGCN)\n\n- **通过集成提高梯度提升的不确定性（ICLR 2021）**\n  - 安德烈·马利宁、柳德米拉·普罗霍连科娃、阿列克谢·乌斯季缅科\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.10562)\n  - \n- **先提升再卷积：梯度提升与图神经网络的结合（ICLR 2021）**\n  - 谢尔盖·伊万诺夫、柳德米拉·普罗霍连科娃\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.08543)\n\n- **GBHT：用于密度估计的梯度提升直方图变换（ICML 2021）**\n  - 崔静怡、韩元·杭、王义森、林周臣\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.05738)\n\n- **面向在线凸优化的提升（ICML 2021）**\n  - 埃拉德·哈赞、卡兰·辛格\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.09305)\n\n- **通过可解释提升实现准确性、可解释性和差分隐私（ICML 2021）**\n  - 哈尔沙·诺里、里奇·卡鲁阿纳、卜志奇、沈茱蒂·韩雯、贾纳尔丹·库尔卡尼\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.09680)\n\n- **SGLB：随机梯度朗之万提升（ICML 2021）**\n  - 阿列克谢·乌斯季缅科、柳德米拉·普罗霍连科娃\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.07248)\n\n- **用于特征蒸馏的自提升（IJCAI 2021）**\n  - 裴玉龙、屈燕云、张俊平\n  - [[论文]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F131)\n\n- **采用局部自适应步长的提升变分推断（IJCAI 2021）**\n  - 吉迪恩·德雷斯德纳、萨乌拉夫·谢卡尔、法比安·佩德雷戈萨、弗朗切斯科·洛卡泰洛、冈纳尔·雷茨\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.09240)\n\n- **用于大规模概率回归的概率梯度提升机（KDD 2021）**\n  - 奥利维尔·斯普兰格斯、塞巴斯蒂安·谢尔特、马尔滕·德·赖克\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.01682)\n\n- **针对多中心糖尿病预测的任务划分梯度提升树（KDD 2021）**\n  - 陈明成、王正辉、赵志云、张卫南、郭西亚伟、沈建、屈艳茹、陆继莉、许敏、于旭、王天歌、李勉、涂伟伟、于勇、毕玉芳、王伟青、宁广\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.07107)\n\n- **与其短视不如更好：通过最优规则提升构建可解释模型（SDM 2021）**\n  - 马里奥·博利、西蒙·特舒瓦、皮埃尔·勒·博迪克、杰弗里·I·韦伯\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.08380)\n\n## 2020年\n\n- **面向知识密集型梯度提升的统一框架：在噪声稀疏领域中利用人类专家（AAAI 2020）**\n  - 哈尔沙·科凯尔、菲利普·奥多姆、杨硕、斯里拉姆·纳塔拉詹\n  - [[论文]](https:\u002F\u002Fpersonal.utdallas.edu\u002F~sriraam.natarajan\u002FPapers\u002FKokel_AAAI20.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fharshakokel\u002FKiGB)\n\n- **实用的联邦梯度提升决策树（AAAI 2020）**\n  - 李钦斌、文泽毅、何炳生\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.04206)\n\n- **隐私保护的梯度提升决策树（AAAI 2020）**\n  - 李钦斌、吴兆敏、文泽毅、何炳生\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.04209)\n\n- **加速梯度提升机（AISTATS 2020）**\n  - 陆海浩、赛·普拉尼特·卡里米雷迪、娜塔莉娅·波诺马列娃、瓦哈布·S·米尔罗克尼\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.08708)\n\n- **多任务梯度提升树的可扩展特征选择（AISTATS 2020）**\n  - 韩翠泽、尼基尔·拉奥、达里亚·索罗基娜、卡尔蒂克·苏比亚恩\n  - [[论文]](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002Fhan20a.html)\n\n- **具有统计保证的残差网络学习中的函数梯度提升（AISTATS 2020）**\n  - 新田敦史、铃木泰治\n  - [[论文]](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002Fnitanda20a.html)\n\n- **使用MaxSAT学习最优决策树及其在AdaBoost中的集成（IJCAI 2020）**\n  - 胡浩、穆罕默德·西亚拉、埃曼纽埃尔·埃布拉尔、玛丽-乔瑟·于盖\n  - [[论文]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F2020\u002F163)\n\n- **MixBoost：基于增强混合的合成过采样方法，用于处理极端类别不平衡问题（ICDM 2020）**\n  - 阿努巴·卡布拉、阿尤什·乔普拉、尼卡什·普里、平凯什·巴贾蒂亚、苏克里蒂·维尔玛、皮尤什·古普塔、巴拉吉·克里希纳穆提\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.01571)\n\n- **用于动力系统控制的梯度提升（ICML 2020）**\n  - 纳曼·阿加瓦尔、娜塔莉·布鲁希姆、埃拉德·哈赞、周璐\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.08720)\n\n- **量子梯度提升（ICML 2020）**\n  - 斯里尼瓦桑·阿鲁纳查拉姆、里夫·迈蒂\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.05056)\n\n- **用于回归任务的增强直方图变换（ICML 2020）**\n  - 蔡宇超、韩元航、杨汉芳、林周辰\n  - [[论文]](https:\u002F\u002Fproceedings.icml.cc\u002Fstatic\u002Fpaper_files\u002Ficml\u002F2020\u002F2360-Paper.pdf)\n\n- **通过追逐梯度来增强Frank-Wolfe算法（ICML 2020）**\n  - 西里尔·W·康贝特、塞巴斯蒂安·波库塔\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.06369)\n\n- **NGBoost：用于概率预测的自然梯度提升（ICML 2020）**\n  - 托尼·杜安、阿瓦蒂·阿南德、黛西·易·丁、坎赫·K·泰、桑杰·巴苏、安德鲁·Y·吴、亚历杭德罗·舒勒\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1910.03225)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fstanfordmlgroup\u002Fngboost)\n\n- **基于后悔最小化的在线无知性梯度提升（NeurIPS 2020）**\n  - 娜塔莉·布鲁希姆、陈欣怡、埃拉德·哈赞、谢伊·莫兰\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.01150)\n\n- **通过目标函数平移来增强一阶优化方法：具有更快最坏情况收敛率的新方案（NeurIPS 2020）**\n  - 周凯文、安东尼·曼-乔·索、詹姆斯·程\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.12061)\n\n- **通过梯度提升进行转导的优化与泛化分析及其在多尺度图神经网络中的应用（NeurIPS 2020）**\n  - 大野健太、铃木泰治\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.08550)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fdelta2323\u002FGB-GNN)\n\n- **梯度提升归一化流（NeurIPS 2020）**\n  - 罗伯特·贾奎托、阿林达姆·班纳吉\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.11896)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Frobert-giaquinto\u002Fgradient-boosted-normalizing-flows)\n\n- **HyperML：一种在双曲空间中用于推荐系统的梯度提升度量学习方法（WSDM 2020）**\n  - 卢卡斯·文·陈、易泰、张帅、高聪、李晓丽\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.01703)\n\n## 2019年\n\n- **利用 LIME 将非单调逻辑程序归纳用于解释提升树模型（AAAI 2019）**\n  - Farhad Shakerin, Gopal Gupta\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.00629)\n\n- **验证梯度提升模型的鲁棒性（AAAI 2019）**\n  - Gil Einziger, Maayan Goldstein, Yaniv Sa'ar, Itai Segall\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.10991.pdf)\n\n- **带军械反馈的在线多分类提升算法（AISTATS 2019）**\n  - Daniel T. Zhang, Young Hun Jung, Ambuj Tewari\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.05290)\n\n- **AdaFair：累积公平自适应提升算法（CIKM 2019）**\n  - Vasileios Iosifidis, Eirini Ntoutsi\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.08982)\n\n- **基于提升树的跨域可解释多任务学习（CIKM 2019）**\n  - Ya-Lin Zhang, Longfei Li\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3357384.3358072)\n\n- **梯度提升决策树的对抗训练（CIKM 2019）**\n  - Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei\n  - [[论文]](https:\u002F\u002Fwww.dais.unive.it\u002F~calzavara\u002Fpapers\u002Fcikm19.pdf)\n\n- **公平对抗梯度树提升算法（ICDM 2019）**\n  - Vincent Grari, Boris Ruf, Sylvain Lamprier, Marcin Detyniecki\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.05369)\n\n- **重制版提升密度估计（ICML 2019）**\n  - Zac Cranko, Richard Nock\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.08178)\n\n- **采用整数算术的无损或量化提升算法（ICML 2019）**\n  - Richard Nock, Robert C. Williamson\n  - [[论文]](http:\u002F\u002Fproceedings.mlr.press\u002Fv97\u002Fnock19a.html)\n\n- **通过提升实现最优最小间隔最大化（ICML 2019）**\n  - Alexander Mathiasen, Kasper Green Larsen, Allan Grønlund\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.10789)\n\n- **Katalyst：针对具有大条件数的非凸问题的凸 Katayusha 提升算法（ICML 2019）**\n  - Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.06754)\n\n- **基于比较的学习中的提升算法（IJCAI 2019）**\n  - Michaël Perrot, Ulrike von Luxburg\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.13333)\n\n- **AugBoost：基于分步特征增强的梯度提升算法（IJCAI 2019）**\n  - Philip Tannor, Lior Rokach\n  - [[论文]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F0493.pdf)\n\n- **基于分段线性回归树的梯度提升算法（IJCAI 2019）**\n  - Yu Shi, Jian Li, Zhize Li\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.05640)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002FGBDT-PL\u002FGBDT-PL)\n\n- **SpiderBoost 和动量：更快速的方差缩减算法（NeurIPS 2019）**\n  - Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh\n  - [[论文]](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8511-spiderboost-and-momentum-faster-variance-reduction-algorithms)\n\n- **内存占用更小的更快提升算法（NeurIPS 2019）**\n  - Julaiti Alafate, Yoav Freund\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.09047)\n\n- **正则化梯度提升算法（NeurIPS 2019）**\n  - Corinna Cortes, Mehryar Mohri, Dmitry Storcheus\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8784-regularized-gradient-boosting)\n\n- **基于间隔的提升分类器泛化下界（NeurIPS 2019）**\n  - Allan Grønlund, Lior Kamma, Kasper Green Larsen, Alexander Mathiasen, Jelani Nelson\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.12518)\n\n- **随机梯度提升中的最小方差采样（NeurIPS 2019）**\n  - Bulat Ibragimov, Gleb Gusev\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F9645-minimal-variance-sampling-in-stochastic-gradient-boosting)\n\n- **通用提升变分推断（NeurIPS 2019）**\n  - Trevor Campbell, Xinglong Li\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.01235)\n\n- **可证明鲁棒的提升决策桩和决策树对抗攻击（NeurIPS 2019）**\n  - Maksym Andriushchenko, Matthias Hein\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.03526)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fmax-andr\u002Fprovably-robust-boosting)\n\n- **块分布式梯度提升决策树（SIGIR 2019）**\n  - Theodore Vasiloudis, Hyunsu Cho, Henrik Boström\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.10522)\n\n- **排序学习的理论与实践：从梯度提升到神经网络及无偏学习（SIGIR 2019）**\n  - Claudio Lucchese, Franco Maria Nardini, Rama Kumar Pasumarthi, Sebastian Bruch, Michael Bendersky, Xuanhui Wang, Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke\n  - [[论文]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F334579610_Learning_to_Rank_in_Theory_and_Practice_From_Gradient_Boosting_to_Neural_Networks_and_Unbiased_Learning)\n\n## 2018年\n- **增强生成模型（AAAI 2018）**\n  - Aditya Grover, Stefano Ermon\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1702.08484.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fermongroup\u002Fbgm)\n\n- **变分推断的提升：优化视角（AISTATS 2018）**\n  - Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.01733)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fratschlab\u002Fboosting-bbvi)\n\n- **多标签排序的在线提升算法（AISTATS 2018）**\n  - Young Hun Jung, Ambuj Tewari\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.08079)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fyhjung88\u002FOnlineMLRBoostingWithVFDT)\n\n- **DualBoost：利用特征权重和弃权弱分类器处理缺失值（CIKM 2018）**\n  - Weihong Wang, Jie Xu, Yang Wang, Chen Cai, Fang Chen\n  - [[论文]](http:\u002F\u002Fdelivery.acm.org\u002F10.1145\u002F3270000\u002F3269319\u002Fp1543-wang.pdf?ip=129.215.164.203&id=3269319&acc=ACTIVE%20SERVICE&key=C2D842D97AC95F7A%2EEB9E991028F4E1F1%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&__acm__=1558633895_f01b39fd47b943fd01eade763a397e04)\n\n- **基于残差网络感知的函数梯度提升（ICML 2018）**\n  - Atsushi Nitanda, Taiji Suzuki\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.09031)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fanitan0925\u002FResFGB)\n\n- **为梯度提升决策树寻找有影响力的训练样本（ICML 2018）**\n  - Boris Sharchilev, Yury Ustinovskiy, Pavel Serdyukov, Maarten de Rijke\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.06640)\n\n- **利用提升理论顺序学习深度ResNet块（ICML 2018）**\n  - Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.04964)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002FJordanAsh\u002Fboostresnet)\n\n- **UCBoost：一种用于随机 bandit 问题的提升方法，以控制复杂度和最优性（IJCAI 2018）**\n  - Fang Liu, Sinong Wang, Swapna Buccapatnam, Ness B. Shroff\n  - [[论文]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2018\u002F0338.pdf)\n  - [[代码]](https:\u002F\u002Fsmpybandits.github.io\u002Fdocs\u002FPolicies.UCBoost.html)\n\n- **面向对话模型的自动评估 AdaBoost（IJCAI 2018）**\n  - Juncen Li, Ping Luo, Ganbin Zhou, Fen Lin, Cheng Niu\n  - [[论文]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2018\u002F0580.pdf)\n\n- **基于自适应提升的集成神经关系抽取（IJCAI 2018）**\n  - Dongdong Yang, Senzhang Wang, Zhoujun Li\n  - [[论文]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2018\u002F0630.pdf)\n\n- **CatBoost：无偏的类别型特征提升算法（NIPS 2018）**\n  - Liudmila Ostroumova Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7898-catboost-unbiased-boosting-with-categorical-features.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fcatboost\u002Fcatboost)\n\n- **具有竞争风险的生存分析中的多任务提升（NIPS 2018）**\n  - Alexis Bellot, Mihaela van der Schaar\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7413-multitask-boosting-for-survival-analysis-with-competing-risks)\n\n- **多层梯度提升决策树（NIPS 2018）**\n  - Ji Feng, Yang Yu, Zhi-Hua Zhou\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7614-multi-layered-gradient-boosting-decision-trees.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fkingfengji\u002FmGBDT)\n\n- **稀疏与低秩张量回归的提升方法（NIPS 2018）**\n  - Lifang He, Kun Chen, Wanwan Xu, Jiayu Zhou, Fei Wang\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.01158)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002FLifangHe\u002FNeurIPS18_SURF)\n\n- **用于有效排序学习的选择性梯度提升（SIGIR 2018）**\n  - Claudio Lucchese, Franco Maria Nardini, Raffaele Perego, Salvatore Orlando, Salvatore Trani\n  - [[论文]](http:\u002F\u002Fquickrank.isti.cnr.it\u002Fselective-data\u002Fselective-SIGIR2018.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fhpclab\u002Fquickrank\u002Fblob\u002Fmaster\u002Fdocumentation\u002Fselective.md)\n\n## 2017年\n- **用于实时多变量时间序列分类的提升算法（AAAI 2017）**\n  - 王海帅、吴俊\n  - [[论文]](https:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI17\u002Fpaper\u002Fdownload\u002F14852\u002F14241)\n\n- **基于主题相关TrAdaBoost的跨领域情感分类（AAAI 2017）**\n  - 黄兴昌、饶阳辉、谢浩然、王德霖、王福礼\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F826c\u002Fc83d98a5c4c7dcc02be1f4dd9c27e2b99670.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fxchhuang\u002Fcross-domain-sentiment-classification)\n\n- **极端梯度提升与行为生物特征识别（AAAI 2017）**\n  - 本杰明·曼宁\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F8c6e\u002F6c887d6d47dda3f0c73297fd4da516fef1ee.pdf)\n\n- **FeaBoost：面向语义分割的特征与标签联合优化（AAAI 2017）**\n  - 牛玉磊、陆志武、黄松芳、高欣、文继荣\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fd566\u002F73be998b3ed38ccbb53551e38758ae8cfc9d.pdf)\n\n- **用于快速近邻搜索的互补哈希表提升算法（AAAI 2017）**\n  - 刘向龙、邓成、穆亚东、李竹金\n  - [[论文]](https:\u002F\u002Faaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI17\u002Fpaper\u002Fview\u002F14336)\n\n- **随机数据流上的梯度提升（AISTATS 2017）**\n  - 胡汉章、孙文、阿伦·文卡特拉曼、马蒂尔·赫伯特、J·安德鲁·巴格内尔\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.00377)\n\n- **BoostVHT：分布式流式决策树的提升算法（CIKM 2017）**\n  - 西奥多·瓦西卢迪斯、福泰尼·贝利加尼、詹马可·德·弗朗西斯基·莫拉莱斯\n  - [[论文]](https:\u002F\u002Fmelmeric.files.wordpress.com\u002F2010\u002F05\u002Fboostvht-boosting-distributed-streaming-decision-trees.pdf)\n\n- **基于尺度不变的多模态多分辨率滤波特征的快速提升检测方法（CVPR 2017）**\n  - 阿瑟·丹尼尔·科斯特亚、罗伯特·瓦尔加、塞尔吉乌·内德夫斯基\n  - [[论文]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2017\u002Fpapers\u002FCostea_Fast_Boosting_Based_CVPR_2017_paper.pdf)\n\n- **BIER：稳健地提升独立嵌入（ICCV 2017）**\n  - 迈克尔·奥皮茨、格奥尔格·瓦尔特纳、霍斯特·波塞格尔、霍斯特·比绍夫\n  - [[论文]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2017\u002Fpapers\u002FOpitz_BIER_-_Boosting_ICCV_2017_paper.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fmop\u002Fbier)\n\n- **噪声数据上提升线性分类器的分析及其在多示例学习中的应用（ICDM 2017）**\n  - 刘锐、苏米娅·雷\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8215501)\n\n- **变分提升：迭代优化后验近似（ICML 2017）**\n  - 安德鲁·C·米勒、尼古拉斯·J·福蒂、瑞安·P·亚当斯\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.06585)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fandymiller\u002Fvboost)\n\n- **提升拟合Q值迭代法（ICML 2017）**\n  - 萨穆埃莱·托萨托、马泰奥·皮罗塔、卡洛·德埃拉莫、马尔切洛·雷斯泰利\n  - [[论文]](http:\u002F\u002Fproceedings.mlr.press\u002Fv70\u002Ftosatto17a.html)\n\n- **具有理论保证和实证性能的简单多分类提升框架（ICML 2017）**\n  - 罗恩·阿佩尔、皮耶特罗·佩罗纳\n  - [[论文]](http:\u002F\u002Fproceedings.mlr.press\u002Fv70\u002Fappel17a.html)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002FGuillaumeCollin\u002FA-Simple-Multi-Class-Boosting-Framework-with-Theoretical-Guarantees-and-Empirical-Proficiency)\n\n- **用于高维稀疏输出的梯度提升决策树（ICML 2017）**\n  - 斯斯、张欢、S·萨提亚·基尔蒂、德鲁夫·马哈詹、英德尔吉特·S·迪隆、何祖慧\n  - [[论文]](http:\u002F\u002Fproceedings.mlr.press\u002Fv70\u002Fsi17a.html)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fspringdaisy\u002FGBDT)\n\n- **基于非负矩阵分解集成的提升算法实现局部主题发现（IJCAI 2017）**\n  - 徐相浩、秋在国、李俊锡、钱丹·K·雷迪\n  - [[论文]](http:\u002F\u002Fdmkd.cs.vt.edu\u002Fpapers\u002FIJCAI17.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FBoostedFactorization)\n\n- **带有语义相关性正则化的零样本学习提升算法（IJCAI 2017）**\n  - 梯皮、李熙、张仲飞（马克）\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.08008)\n\n- **BDT：兼具高精度与评分效率的梯度提升决策表（KDD 2017）**\n  - 刘寅、米哈伊尔·奥布霍夫\n  - [[论文]](https:\u002F\u002Fyinlou.github.io\u002Fpapers\u002Flou-kdd17.pdf)\n\n- **CatBoost：支持类别型特征的梯度提升算法（NIPS 2017）**\n  - 安娜·维罗妮卡·多罗古什、瓦西里·叶尔绍夫、安德烈·古林\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.11363)\n  - [[代码]](https:\u002F\u002Fcatboost.ai\u002F)\n\n- **成本高效的梯度提升算法（NIPS 2017）**\n  - 斯文·彼得、费兰·迭戈、弗雷德·A·汉普雷希特、博阿兹·纳德勒\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F6753-cost-efficient-gradient-boosting)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fsvenpeter42\u002FLightGBM-CEGB)\n\n- **AdaGAN：生成模型的提升算法（NIPS 2017）**\n  - 伊利亚·O·托尔斯季欣、西尔万·盖利、奥利维埃·布斯凯、卡尔-约翰·西蒙-加布里埃尔、伯恩哈德·舍尔科普夫\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.02386)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Ftolstikhin\u002Fadagan)\n\n- **LightGBM：一种高效的梯度提升决策树（NIPS 2017）**\n  - 郭霖、孟奇、托马斯·芬利、王泰峰、陈伟、马卫东、叶启伟、刘铁燕\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree)\n  - [[代码]](https:\u002F\u002Flightgbm.readthedocs.io\u002Fen\u002Flatest\u002F)\n\n- **核提升算法的提前停止：基于局部复杂度的一般性分析（NIPS 2017）**\n  - 魏宇婷、杨凡妮、马丁·J·韦恩赖特\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.01543)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Ffanny-yang\u002FEarlyStoppingRKHS)\n\n- **在线多分类提升算法（NIPS 2017）**\n  - 郑永勋、杰克·戈茨、安布杰·特瓦里\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F6693-online-multiclass-boosting.pdf)\n\n- **堆叠袋装与提升森林以实现高效自动化分类（SIGIR 2017）**\n  - 拉斐尔·R·坎波斯、塞尔吉奥·D·卡努托、蒂亚戈·萨列斯、克莱布森·C·A·德·萨、马科斯·安德烈·贡萨尔维斯\n  - [[论文]](https:\u002F\u002Fhomepages.dcc.ufmg.br\u002F~rcampos\u002Fpapers\u002Fsigir2017\u002Fappendix.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fraphaelcampos\u002Fstacking-bagged-boosted-forests)\n\n- **GB-CENT：梯度提升的类别型嵌入与数值型决策树（WWW 2017）**\n  - 赵倩、石悦、洪亮杰\n  - [[论文]](http:\u002F\u002Fpapers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com\u002Fproceedings\u002Fp1311.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fgrouplens\u002Fsamantha)\n\n## 2016年\n- **用于多分辨率行人检测的分组代价敏感提升算法（AAAI 2016）**\n  - 朱超、彭宇欣\n  - [[论文]](https:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI16\u002Fpaper\u002FviewFile\u002F11898\u002F12146)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fnnikolaou\u002FCost-sensitive-Boosting-Tutorial)\n\n- **通信高效的分布式无假设提升算法（AISTATS 2016）**\n  - 陈尚泽、玛丽亚-弗洛里娜·巴尔坎、周德鸿\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1506.06318)\n\n- **用于标签分布学习的逻辑提升回归（CVPR 2016）**\n  - 邢超、耿欣、薛辉\n  - [[论文]](https:\u002F\u002Fzpascal.net\u002Fcvpr2016\u002FXing_Logistic_Boosting_Regression_CVPR_2016_paper.pdf)\n\n- **结构化回归梯度提升（CVPR 2016）**\n  - 费兰·迭戈、弗雷德·A·汉普雷希特\n  - [[论文]](https:\u002F\u002Fhci.iwr.uni-heidelberg.de\u002Fsites\u002Fdefault\u002Ffiles\u002Fpublications\u002Ffiles\u002F1037872734\u002Fdiego_16_structured.pdf)\n\n- **L-EnsNMF：基于非负矩阵分解集成的增强局部主题发现（ICDM 2016）**\n  - 徐相浩、秋在角、李俊锡、钱丹·K·雷迪\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7837872)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002FBoostedFactorization)\n\n- **用于权重和目标学习的元梯度提升决策树模型（ICML 2016）**\n  - 尤里·乌斯季诺夫斯基、瓦伦蒂娜·费多罗娃、格列布·古谢夫、帕维尔·谢尔久科夫\n  - [[论文]](http:\u002F\u002Fproceedings.mlr.press\u002Fv48\u002Fustinovskiy16.html)\n\n- **用于多任务学习的广义字典与提升方法（IJCAI 2016）**\n  - 王博宇、乔埃尔·派诺\n  - [[论文]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F16\u002FPapers\u002F299.pdf)\n\n- **面向分类的自定步调提升学习（IJCAI 2016）**\n  - 柘皮、李曦、张忠飞、孟德玉、吴飞、肖军、庄宇婷\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F31b6\u002Fab4a0771d5b7405cacdd12c398b1c832729d.pdf)\n\n- **交互式鞅提升（IJCAI 2016）**\n  - 阿希什·库尔卡尼、普什帕克·布拉恩格、加内什·拉马克里希南\n  - [[论文]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F16\u002FPapers\u002F124.pdf)\n\n- **在线提升的最优与自适应算法（IJCAI 2016）**\n  - 阿丽娜·贝伊格尔齐默、萨滕·卡莱、海鹏·卢\n  - [[论文]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F16\u002FPapers\u002F614.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002FVowpalWabbit\u002Fvowpal_wabbit\u002Fblob\u002Fmaster\u002Fvowpalwabbit\u002Fboosting.cc)\n\n- **评分增强的潜在主题：利用评分和评论理解用户与物品（IJCAI 2016）**\n  - 谭云志、张敏、刘一群、马绍平\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fdb63\u002F89e0ca49ec0e4686e40604e7489cb4c0729d.pdf)\n\n- **XGBoost：一个可扩展的树提升系统（KDD 2016）**\n  - 陈天奇、卡洛斯·格斯特林\n  - [[论文]](https:\u002F\u002Fwww.kdd.org\u002Fkdd2016\u002Fpapers\u002Ffiles\u002Frfp0697-chenAemb.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fdmlc\u002Fxgboost)\n\n- **用于在线控制实验中方差缩减的提升决策树回归调整（KDD 2016）**\n  - 阿列克谢·波亚尔科夫、阿列克谢·德鲁察、安德烈·哈利亚文、格列布·古谢夫、帕维尔·谢尔久科夫\n  - [[论文]](https:\u002F\u002Fwww.kdd.org\u002Fkdd2016\u002Fpapers\u002Ffiles\u002Fadf0653-poyarkovA.pdf)\n\n- **带弃权的提升方法（NIPS 2016）**\n  - 科琳娜·科尔特斯、朱莉娅·德萨尔沃、梅赫里亚尔·莫赫里\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F6336-boosting-with-abstention)\n\n- **SEBOOST——利用子空间优化技术进行随机学习的提升方法（NIPS 2016）**\n  - 埃拉德·理查森、罗姆·赫尔斯科维茨、鲍里斯·金斯堡、迈克尔·齐布列夫斯基\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F6109-seboost-boosting-stochastic-learning-using-subspace-optimization-techniques.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Feladrich\u002Fseboost)\n\n- **用于面部动作单元识别的增量提升卷积神经网络（NIPS 2016）**\n  - 韩世宗、孟子博、艾哈迈德-谢哈布·汗、谭燕\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.05395)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fsjsingh91\u002FIB-CNN)\n\n- **广义BROOF-L2R：基于提升与随机森林的学习排序通用框架（SIGIR 2016）**\n  - 克莱布森·C·A·德·萨、马科斯·安德烈·贡萨尔维斯、丹尼尔·哈维尔·德·索萨、蒂亚戈·萨列斯\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2911540)\n\n## 2015年\n\n- **用于随时迁移和多任务学习的在线提升算法（AAAI 2015）**\n  - 王博宇、乔埃尔·派诺\n  - [[论文]](https:\u002F\u002Fwww.cs.mcgill.ca\u002F~jpineau\u002Ffiles\u002Fbwang-aaai15.pdf)\n\n- **一种用于遮挡处理的行人检测增强型多任务模型（AAAI 2015）**\n  - 朱超、彭宇欣\n  - [[论文]](https:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI15\u002Fpaper\u002FviewFile\u002F9879\u002F9825)\n\n- **条件随机场的高效二阶梯度提升方法（AISTATS 2015）**\n  - 陈天奇、萨米尔·辛格、本·塔斯卡、卡洛斯·古斯特林\n  - [[论文]](http:\u002F\u002Fproceedings.mlr.press\u002Fv38\u002Fchen15b.html)\n\n- **基于增强归纳矩阵补全的Tumblr博客推荐（CIKM 2015）**\n  - 申东赫、苏莱曼·切廷塔什、李匡志、英德尔吉特·S·迪隆\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2806578)\n\n- **基于基映射的物体检测增强方法（CVPR 2015）**\n  - 任浩宇、李泽年\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7298766)\n\n- **基于在线梯度提升决策树的分割跟踪法（ICCV 2015）**\n  - 孙济娜、郑一彩、朴佳英、韩宝亨\n  - [[论文]](https:\u002F\u002Fwww.cv-foundation.org\u002Fopenaccess\u002Fcontent_iccv_2015\u002Fpapers\u002FSon_Tracking-by-Segmentation_With_Online_ICCV_2015_paper.pdf)\n  - [[代码]](http:\u002F\u002Fcvlab.postech.ac.kr\u002Fresearch\u002Fogbdt_track\u002F)\n\n- **学习增强丝状结构分割（ICCV 2015）**\n  - 顾琳、程丽\n  - [[论文]](https:\u002F\u002Fisg.nist.gov\u002FBII_2015\u002FwebPages\u002Fpages\u002F2015_BII_program\u002FPDFs\u002FDay_3\u002FSession_9\u002FAbstract_Gu_Lin.pdf)\n\n- **在线提升的最优与自适应算法（ICML 2015）**\n  - 阿丽娜·贝伊盖尔齐默、萨蒂恩·卡莱、罗海鹏\n  - [[论文]](http:\u002F\u002Fproceedings.mlr.press\u002Fv37\u002Fbeygelzimer15.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002FVowpalWabbit\u002Fvowpal_wabbit\u002Fblob\u002Fmaster\u002Fvowpalwabbit\u002Fboosting.cc)\n\n- **拉德马赫观测、隐私数据与提升（ICML 2015）**\n  - 理查德·诺克、乔治奥·帕特里尼、阿里克·弗里德曼\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1502.02322)\n\n- **用于剪接位点计算预测的增强型分类受限玻尔兹曼机（ICML 2015）**\n  - 李泰勋、尹成路\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fd0ad\u002Fbeef3053e98dd88ff74f42744417bc65a729.pdf)\n\n- **一种用于半监督分类的直接提升方法（IJCAI 2015）**\n  - 赵绍丹、夏田、李忠良、王绍军\n  - [[论文]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F15\u002FPapers\u002F565.pdf)\n\n- **一种基于隐式反馈的商品推荐增强算法（IJCAI 2015）**\n  - 刘勇、赵培琳、孙爱欣、苗春燕\n  - [[论文]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F15\u002FPapers\u002F255.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Frecommenders)\n\n- **预算约束下提升器的训练时间优化（IJCAI 2015）**\n  - 黄毅、布莱恩·鲍尔斯、列夫·雷津\n  - [[论文]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F15\u002FPapers\u002F504.pdf)\n\n- **随机森林与提升树的最优动作提取（KDD 2015）**\n  - 崔志诚、陈文林、何宇杰、陈义新\n  - [[论文]](https:\u002F\u002Fwww.cse.wustl.edu\u002F~ychen\u002Fpublic\u002FOAE.pdf)\n\n- **在线梯度提升（NIPS 2015）**\n  - 阿丽娜·贝伊盖尔齐默、埃拉德·哈赞、萨蒂恩·卡莱、罗海鹏\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1506.04820)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fcrm416\u002Fonline_boosting)\n\n- **BROOF：利用袋外误差提升与随机森林实现高效的自动化分类（SIGIR 2015）**\n  - 蒂亚戈·萨列斯、马科斯·安德烈·贡萨尔维斯、维克托·罗德里格斯、莱昂纳多·C·达·罗沙\n  - [[论文]](https:\u002F\u002Fhomepages.dcc.ufmg.br\u002F~tsalles\u002Fbroof\u002Fappendix.pdf)\n\n- **通过深度理解内容和用户来提升搜索效果（WSDM 2015）**\n  - 朱凯华\n  - [[论文]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F282482189_Boosting_Search_with_Deep_Understanding_of_Contents_and_Users)\n\n## 2014年\n- **关于提升稀疏奇偶校验问题（AAAI 2014）**\n  - Lev Reyzin\n  - [[论文]](https:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI14\u002Fpaper\u002Fview\u002F8587)\n\n- **用于阿尔茨海默病诊断的联合耦合特征表示与耦合提升方法（CVPR 2014）**\n  - Shi Yinghuan, Suk Heung-Il, Gao Yang, Shen Dinggang\n  - [[论文]](https:\u002F\u002Fwww.cv-foundation.org\u002Fopenaccess\u002Fcontent_cvpr_2014\u002Fpapers\u002FShi_Joint_Coupled-Feature_Representation_2014_CVPR_paper.pdf)\n\n- **从类别到个体的实时统一提升方法（CVPR 2014）**\n  - Hall David, Perona Pietro\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6909424)\n  - [[代码]](http:\u002F\u002Fwww.vision.caltech.edu\u002F~dhall\u002Fprojects\u002FCategoriesToIndividuals\u002F)\n\n- **高效的基于示例的人脸检测提升方法（CVPR 2014）**\n  - Li Haoxiang, Lin Zhe, Brandt Jonathan, Shen Xiaohui, Hua Gang\n  - [[论文]](http:\u002F\u002Fusers.eecs.northwestern.edu\u002F~xsh835\u002Fassets\u002Fcvpr14_exemplarfacedetection.pdf)\n\n- **基于提升的深度信念网络的人脸表情识别（CVPR 2014）**\n  - Liu Ping, Han Shizhong, Meng Zibo, Tong Yan\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F6909629)\n\n- **面向目标检测的置信度加权多示例提升方法（CVPR 2014）**\n  - Ali Karim, Saenko Kate\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6909708)\n\n- **AdaBoost.MH的回归：多分类汉明树（ICLR 2014）**\n  - Kégl Balázs\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1312.6086.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Faciditeam\u002Facidano\u002Fblob\u002Fmaster\u002Facidano\u002Futils\u002Fcost.py)\n\n- **深度提升（ICML 2014）**\n  - Cortes Corinna, Mohri Mehryar, Syed Umar\n  - [[论文]](http:\u002F\u002Fproceedings.mlr.press\u002Fv32\u002Fcortesb14.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fdeepboost)\n\n- **LogitBoost、MART及其变体的收敛速率分析（ICML 2014）**\n  - Sun Peng, Zhang Tong, Zhou Jie\n  - [[论文]](http:\u002F\u002Fproceedings.mlr.press\u002Fv32\u002Fsunc14.pdf)\n\n- **利用在线二分类学习器进行多分类赌博机问题的提升（ICML 2014）**\n  - Chen Shang-Tse, Lin Hsuan-Tien, Lu Chi-Jen\n  - [[论文]](https:\u002F\u002Fwww.cc.gatech.edu\u002F~schen351\u002Fpaper\u002Ficml14boost.pdf)\n\n- **多步自回归预测的提升方法（ICML 2014）**\n  - Ben Taieb Souhaib, Hyndman Rob J.\n  - [[论文]](http:\u002F\u002Fproceedings.mlr.press\u002Fv32\u002Ftaieb14.pdf)\n\n- **用于判别宏动作发现的动态规划提升方法（ICML 2014）**\n  - Lefakis Leonidas, Fleuret François\n  - [[论文]](http:\u002F\u002Fproceedings.mlr.press\u002Fv32\u002Flefakis14.html)\n\n- **针对代价敏感多分类提升的猜忌损失函数（ICML 2014）**\n  - Beijbom Oscar, Saberian Mohammad J., Kriegman David J., Vasconcelos Nuno\n  - [[论文]](http:\u002F\u002Fproceedings.mlr.press\u002Fv32\u002Fbeijbom14.pdf)\n\n- **一种直接优化的多分类提升方法（KDD 2014）**\n  - Zhai Shaodan, Xia Tian, Wang Shaojun\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2623689)\n\n- **梯度提升特征选择（KDD 2014）**\n  - Xu Zhixiang Eddie, Huang Gao, Weinberger Kilian Q., Zheng Alice X.\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.04055)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fdmlc\u002Fxgboost)\n\n- **多分类深度提升（NIPS 2014）**\n  - Kuznetsov Vitaly, Mohri Mehryar, Syed Umar\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F5514-multi-class-deep-boosting)\n\n- **通过提升分解高维混合物及其在人脑扩散加权MRI中的应用（NIPS 2014）**\n  - Zheng Charles Y., Pestilli Franco, Rokem Ariel\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F5506-deconvolution-of-high-dimensional-mixtures-via-boosting-with-application-to-diffusion-weighted-mri-of-human-brain)\n\n- **在线学习的漂移博弈分析及其在提升中的应用（NIPS 2014）**\n  - Luo Haipeng, Schapire Robert E.\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1406.1856)\n\n- **基于在线学习的提升框架（NIPS 2014）**\n  - Naghibi Mohamadpoor Tofigh, Pfister Beat\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F5512-a-boosting-framework-on-grounds-of-online-learning.pdf)\n\n- **梯度提升因子分解机（RECSYS 2014）**\n  - Cheng Chen, Xia Fen, Zhang Tong, King Irwin, Lyu Michael R.\n  - [[论文]](http:\u002F\u002Ftongzhang-ml.org\u002Fpapers\u002Frecsys14-fm.pdf)\n\n## 2013年\n\n- **提升二值关键点描述符（CVPR 2013）**\n  - Trzcinski Tomasz, Christoudias C. Mario, Fua Pascal, Lepetit Vincent\n  - [[论文]](https:\u002F\u002Fcvlab.epfl.ch\u002Fresearch\u002Fpage-90554-en-html\u002Fresearch-detect-binboost\u002F)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fbiotrump\u002Fcvlab-BINBOOST)\n\n- **PerturBoost：云环境中的实用保密分类器学习（ICDM 2013）**\n  - Chen Keke, Guo Shumin\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6729587)\n\n- **利用相似性学习的多类半监督提升方法（ICDM 2013）**\n  - Tanha Jafar, Saberian Mohammad Javad, van Someren Maarten\n  - [[论文]](https:\u002F\u002Fwww.cse.msu.edu\u002F~rongjin\u002Fpublications\u002FMultiClass-08.pdf)\n\n- **提升中决策函数的评估时间节省：基学习器的表示与重排（ICML 2013）**\n  - Sun Peng, Zhou Jie\n  - [[论文]](http:\u002F\u002Fproceedings.mlr.press\u002Fv28\u002Fsun13.pdf)\n\n- **利用梯度提升进行通用功能矩阵分解（ICML 2013）**\n  - Chen Tianqi, Li Hang, Yang Qiang, Yu Yong\n  - [[论文]](http:\u002F\u002Fw.hangli-hl.com\u002Fuploads\u002F3\u002F1\u002F6\u002F8\u002F3168008\u002Ficml_2013.pdf)\n\n- **间隔、收缩与提升（ICML 2013）**\n  - Telgarsky Matus\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1303.4172)\n\n- **快速提升决策树——提前剪枝表现不佳的特征（ICML 2013）**\n  - Appel Ron, Fuchs Thomas J., Dollár Piotr, Perona Pietro\n  - [[论文]](http:\u002F\u002Fproceedings.mlr.press\u002Fv28\u002Fappel13.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fpdollar\u002Ftoolbox\u002Fblob\u002Fmaster\u002Fclassify\u002FadaBoostTrain.m)\n\n- **人类提升（ICML 2013）**\n  - Pareek Harsh H., Ravikumar Pradeep\n  - [[论文]](https:\u002F\u002Fwww.cs.cmu.edu\u002F~pradeepr\u002Fpaperz\u002Fhumanboosting.pdf)\n\n- **用于微博活动分类的协同提升方法（KDD 2013）**\n  - Song Yangqiu, Lu Zhengdong, Leung Cane Wing-ki, Yang Qiang\n  - [[论文]](http:\u002F\u002Fchbrown.github.io\u002Fkdd-2013-usb\u002Fkdd\u002Fp482.pdf)\n\n- **直接0-1损失最小化与间隔最大化结合提升（NIPS 2013）**\n  - Zhai Shaodan, Xia Tian, Tan Ming, Wang Shaojun\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F5214-direct-0-1-loss-minimization-and-margin-maximization-with-boosting)\n\n- **水库提升：介于在线与离线集成学习之间（NIPS 2013）**\n  - Lefakis Leonidas, Fleuret François\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F5215-reservoir-boosting-between-online-and-offline-ensemble-learning)\n\n- **基于提升的非线性领域适应（NIPS 2013）**\n  - Becker Carlos J., Christoudias C. Mario, Fua Pascal\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F5200-non-linear-domain-adaptation-with-boosting)\n\n- **标签噪声存在下的提升（UAI 2013）**\n  - Bootkrajang Jakramate, Kabán Ata\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1309.6818)\n\n## 2012年\n- **用于行人检测的上下文增强方法（CVPR 2012）**\n  - 丁媛媛，肖静\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.308.5611&rep=rep1&type=pdf)\n\n- **用于目标检测中多尺度LBP直方图特征选择的收缩提升方法（CVPR 2012）**\n  - 陈耿恒，横光澄夫，松本雄一，田村肇\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6248061)\n\n- **用于显著性估计的自底向上与自顶向下视觉特征提升方法（CVPR 2012）**\n  - Ali Borji\n  - [[论文]](http:\u002F\u002Filab.usc.edu\u002Fborji\u002Fpapers\u002Fcvpr-2012-BUModel-v4.pdf)\n\n- **用于同时进行特征提取与选择的提升算法（CVPR 2012）**\n  - Mohammad J. Saberian, Nuno Vasconcelos\n  - [[论文]](http:\u002F\u002Fsvcl.ucsd.edu\u002Fpublications\u002Fconference\u002F2012\u002Fcvpr\u002FSOPBoost.pdf)\n\n- **通过组稀疏性在多分类提升中共享特征（CVPR 2012）**\n  - Sakrapee Paisitkriangkrai, 沈春华, Anton van den Hengel\n  - [[论文]](https:\u002F\u002Fcs.adelaide.edu.au\u002F~paulp\u002Fpublications\u002Fpubs\u002Fsharing_cvpr2012.pdf)\n\n- **利用提升算法的假设间隔进行特征加权与选择（ICDM 2012）**\n  - Malak Alshawabkeh, Javed A. Aslam, Jennifer G. Dy, David R. Kaeli\n  - [[论文]](http:\u002F\u002Fwww.ece.neu.edu\u002Ffac-ece\u002Fjdy\u002Fpapers\u002Falshawabkeh-ICDM2012.pdf)\n\n- **一种用于多分类半监督学习的AdaBoost算法（ICDM 2012）**\n  - Jafar Tanha, Maarten van Someren, Hamideh Afsarmanesh\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6413799)\n\n- **AOSO-LogitBoost：面向多分类问题的自适应一对一双LogitBoost（ICML 2012）**\n  - 孙鹏，Mark D. Reid, 周杰\n  - [[论文]](AOSO-LogitBoost：面向多分类问题的自适应一对一双LogitBoost)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fpengsun\u002FAOSOLogitBoost)\n\n- **一种具有理论依据的在线提升算法（ICML 2012）**\n  - 陈尚泽，林轩田，卢致仁\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1206.6422)\n\n- **利用提升技巧学习图像描述子（NIPS 2012）**\n  - Tomasz Trzcinski, C. Mario Christoudias, Vincent Lepetit, Pascal Fua\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4848-learning-image-descriptors-with-the-boosting-trick.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fbiotrump\u002Fcvlab-BINBOOST)\n\n- **基于矩阵范数正则化的加速训练：一种提升方法（NIPS 2012）**\n  - 张新华，于耀良，Dale Schuurmans\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4663-accelerated-training-for-matrix-norm-regularization-a-boosting-approach)\n\n- **通过梯度提升一致性从异构数据源中学习（SDM 2012）**\n  - 石晓晓，Jean-François Paiement, David Grangier, Philip S. Yu\n  - [[论文]](http:\u002F\u002Fdavid.grangier.info\u002Fpapers\u002F2012\u002Fshi_sdm_2012.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002FPriyeshV\u002FGBDT-CC)\n\n## 2011年\n- **基于任务的提升算法在学习任务间的选择性迁移（AAAI 2011）**\n  - 埃里克·伊顿，玛丽·德斯贾丹\n  - [[论文]](http:\u002F\u002Fwww.cis.upenn.edu\u002F~eeaton\u002Fpapers\u002FEaton2011Selective.pdf)\n\n- **将提升回归树融入生态潜变量模型（AAAI 2011）**\n  - 丽贝卡·A·哈奇森，刘丽萍，托马斯·G·迪特里希\n  - [[论文]](https:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI11\u002Fpaper\u002FviewFile\u002F3711\u002F4086)\n\n- **FlowBoost——从稀疏标注视频中学习外观特征（CVPR 2011）**\n  - 卡里姆·阿里，大卫·哈斯勒，弗朗索瓦·弗勒雷\n  - [[论文]](http:\u002F\u002Fwww.karimali.org\u002Fpublications\u002FAHF_CVPR11.pdf)\n\n- **用于度量学习的低秩半正定矩阵上的AdaBoost（CVPR 2011）**\n  - 毕金波，吴迪佳，卢乐，刘美珠，陶一鸣，马蒂亚斯·沃尔夫\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=5995363)\n\n- **用于目标定位的提升局部结构化HOG-LBP（CVPR 2011）**\n  - 张俊杰，黄凯琪，于一楠，谭铁牛\n  - [[论文]](http:\u002F\u002Fwww.cbsr.ia.ac.cn\u002Fusers\u002Fynyu\u002F1682.pdf)\n\n- **完全校正多分类提升算法的直接公式（CVPR 2011）**\n  - 申春华，郝志辉\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?arnumber=5995554)\n\n- **门控分类器：高类内变异下的提升方法（CVPR 2011）**\n  - 奥斯卡·M·丹尼尔松，巴巴克·拉索尔扎德，斯特凡·卡尔松\n  - [[论文]](http:\u002F\u002Fwww.nada.kth.se\u002Fcvap\u002Fcvg\u002Fpapers\u002FdanielssonCVPR11.pdf)\n\n- **TaylorBoost：具有显式间隔控制的一阶和二阶提升算法（CVPR 2011）**\n  - 穆罕默德·J·萨贝里安，哈迈德·马斯纳迪-希拉齐，努诺·瓦斯科塞洛斯\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5995605)\n  - [[代码]](https:\u002F\u002Fpythonhosted.org\u002Fbob.learn.boosting\u002F)\n\n- **利用全Bregman散度进行鲁棒高效的正则化提升（CVPR 2011）**\n  - 刘美珠，巴巴·C·维穆里\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5995686)\n\n- **区别对待样本：基于半监督在线CovBoost的目标跟踪（ICCV 2011）**\n  - 李国荣，秦磊，黄庆明，庞军彪，蒋树强\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6126297)\n\n- **LinkBoost：一种新颖的代价敏感提升框架，用于社区级网络链接预测（ICDM 2011）**\n  - 普拉卡什·曼达亚姆·科马尔，潘宁·谭，阿尼尔·K·贾因\n  - [[论文]](http:\u002F\u002Fwww.cse.msu.edu\u002F~ptan\u002Fpapers\u002Ficdm2011.pdf)\n\n- **通过函数梯度提升学习马尔可夫逻辑网络（ICDM 2011）**\n  - 图沙尔·科特，斯里拉姆·纳塔拉扬，克里斯蒂安·克尔斯廷，朱德·W·沙夫利克\n  - [[论文]](https:\u002F\u002Fgithub.com\u002Fstarling-lab\u002FBoostSRL)\n  - [[代码]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6137236)\n\n- **预算内的提升：面向特征高效预测的采样方法（ICML 2011）**\n  - 列夫·雷津\n  - [[论文]](http:\u002F\u002Fwww.icml-2011.org\u002Fpapers\u002F348_icmlpaper.pdf)\n\n- **基于输出编码的铰链损失多分类提升（ICML 2011）**\n  - 高天石，达芙妮·科勒\n  - [[论文]](http:\u002F\u002Fai.stanford.edu\u002F~tianshig\u002Fpapers\u002FmulticlassHingeBoost-ICML2011.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fmemect\u002Fhao\u002Fblob\u002Fmaster\u002Fawesome\u002Fmulticlass-boosting.md)\n\n- **凸优化问题的广义提升算法（ICML 2011）**\n  - 亚历山大·格鲁布，德鲁·巴格内尔\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1105.2054.pdf)\n\n- **关系领域中的模仿学习：一种函数梯度提升方法（IJCAI 2011）**\n  - 斯里拉姆·纳塔拉扬，萨凯特·乔希，普拉萨德·塔德帕利，克里斯蒂安·克尔斯廷，朱德·W·沙夫利克\n  - [[论文]](http:\u002F\u002Fftp.cs.wisc.edu\u002Fmachine-learning\u002Fshavlik-group\u002Fnatarajan.ijcai11.pdf)\n\n- **最大自适应采样的提升方法（NIPS 2011）**\n  - 查尔斯·杜布，弗朗索瓦·弗勒雷\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4310-boosting-with-maximum-adaptive-sampling)\n\n- **提升算法的快速收敛性（NIPS 2011）**\n  - 马图斯·泰尔加斯基\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4343-the-fast-convergence-of-boosting)\n\n- **ShareBoost：基于特征共享的高效多分类学习（NIPS 2011）**\n  - 沙伊·沙列夫-施瓦茨，约纳坦·韦克斯勒，阿姆农·沙舒阿\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4213-shareboost-efficient-multiclass-learning-with-feature-sharing)\n\n- **多分类提升：理论与算法（NIPS 2011）**\n  - 穆罕默德·J·萨贝里安，努诺·瓦斯科塞洛斯\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4450-multiclass-boosting-theory-and-algorithms.pdf)\n\n- **方差惩罚型AdaBoost（NIPS 2011）**\n  - 潘纳加达塔·K·希瓦斯瓦米，托尼·杰巴拉\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4207-variance-penalizing-adaboost.pdf)\n\n- **MKBoost：多核提升框架（SDM 2011）**\n  - 夏浩，史蒂文·C·H·霍伊\n  - [[论文]](https:\u002F\u002Fink.library.smu.edu.sg\u002Fcgi\u002Fviewcontent.cgi?article=3280&context=sis_research)\n\n- **一种改进伪相关反馈的提升方法（SIGIR 2011）**\n  - 吕元华，翟成祥，陈婉\n  - [[论文]](http:\u002F\u002Fwww.tyr.unlu.edu.ar\u002FtallerIR\u002F2012\u002Fpapers\u002Fpseudorelevance.pdf)\n\n- **为高精度、低方差排序模型而采用的梯度提升树袋装法（SIGIR 2011）**\n  - 亚瑟·甘吉萨法尔，里奇·卡鲁阿纳，克里斯蒂娜·维代拉·洛佩斯\n  - [[论文]](http:\u002F\u002Fwww.ccs.neu.edu\u002Fhome\u002Fvip\u002Fteach\u002FMLcourse\u002F4_boosting\u002Fmaterials\u002Fbagging_lmbamart_jforests.pdf)\n\n- **作为专家产品之和的提升方法（UAI 2011）**\n  - 纳拉亚南·乌尼·埃达昆尼，加里·布朗，蒂姆·科瓦奇\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1202.3716)\n\n- **用于Web搜索排名的并行提升回归树（WWW 2011）**\n  - 斯蒂芬·泰里，基利安·Q·温伯格，库纳尔·阿格拉瓦尔，珍妮弗·佩金\n  - [[论文]](http:\u002F\u002Fwww.cs.cornell.edu\u002F~kilian\u002Fpapers\u002Ffr819-tyreeA.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002FYS-L\u002Fpgbm)\n\n## 2010年\n- **探索性行为的提升效应（AAAI 2010）**\n  - Jivko Sinapov、Alexander Stoytchev\n  - [[论文]](https:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI10\u002Fpaper\u002Fdownload\u002F1777\u002F2265)\n\n- **基于提升算法的机器翻译系统组合（ACL 2010）**\n  - 童晓、朱静波、朱木华、王慧珍\n  - [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP10-1076)\n\n- **BagBoo：一种可扩展的混合 Bagging-Boosting 模型（CIKM 2010）**\n  - 德米特里·尤里耶维奇·帕夫洛夫、阿列克谢·戈罗季洛夫、克利夫·A·布伦克\n  - [[论文]](http:\u002F\u002Fcache-default03h.cdn.yandex.net\u002Fdownload.yandex.ru\u002Fcompany\u002Fa_scalable_hybrid_bagging_the_boosting_model.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Farogozhnikov\u002Finfiniteboost)\n\n- **行星图像中陨石坑的自动检测：一种结合特征选择和提升算法的嵌入式框架（CIKM 2010）**\n  - 魏丁、托马什·F·斯特平斯基、洛伦索·P·C·班德拉、里卡多·维拉尔塔、吴友喜、陆振宇、曹天宇\n  - [[论文]](https:\u002F\u002Fwww.uh.edu\u002F~rvilalta\u002Fpapers\u002F2010\u002Fcikm10.pdf)\n\n- **利用提升回归与图模型进行人脸关键点检测（CVPR 2010）**\n  - 米歇尔·弗朗索瓦·瓦尔斯塔、布赖斯·马丁内斯、哈维尔·比内法、玛雅·潘蒂奇\n  - [[论文]](https:\u002F\u002Fibug.doc.ic.ac.uk\u002Fmedia\u002Fuploads\u002Fdocuments\u002FCVPR-2010-ValstarEtAl-CAMERA.pdf)\n\n- **用于多源迁移学习的提升算法（CVPR 2010）**\n  - 姚毅、詹弗兰科·多雷托\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5539857)\n\n- **基于提升随机蕨树的高效旋转不变目标检测（CVPR 2010）**\n  - 迈克尔·维利亚米扎尔、弗朗切斯科·莫雷诺-诺格尔、胡安·安德拉德-塞托、阿尔贝托·桑费柳\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.307.4002&rep=rep1&type=pdf)\n\n- **用于多视角目标检测的隐式层次提升算法（CVPR 2010）**\n  - 哈维埃·佩罗通、马克·斯图尔泽尔、米歇尔·鲁克斯\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5540115)\n\n- **在线半监督多实例提升算法（CVPR 2010）**\n  - 伯恩哈德·蔡斯尔、克里斯蒂安·莱斯特纳、阿米尔·萨法里、霍斯特·比绍夫\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5539860)\n\n- **在线多分类 LPBoost（CVPR 2010）**\n  - 阿米尔·萨法里、马丁·戈德茨、托马斯·波克、克里斯蒂安·莱斯特纳、霍斯特·比绍夫\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.165.8939&rep=rep1&type=pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Famirsaffari\u002Fonline-multiclass-lpboost)\n\n- **用于提升算法的同伦正则化（ICDM 2010）**\n  - 王铮、宋阳秋、张长水\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5694094)\n\n- **利用局部数据不确定性提升全局异常检测（ICDM 2010）**\n  - 刘博、尹杰、肖燕山、曹龙兵、余Philip S.\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5693984)\n\n- **采用收紧的 L0-松弛惩罚项的提升分类器（ICML 2010）**\n  - 诺姆·戈德堡、乔纳森·埃克施泰因\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F11df\u002Faed4ec2a2f72878789fa3a54d588d693bdda.pdf)\n\n- **用于回归迁移的提升算法（ICML 2010）**\n  - 大卫·帕多、彼得·斯通\n  - [[论文]](https:\u002F\u002Fwww.cs.utexas.edu\u002F~dpardoe\u002Fpapers\u002FICML10.pdf)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fjay15summer\u002FTwo-stage-TrAdaboost.R2)\n\n- **用于训练深度模块化网络的提升反向传播学习（ICML 2010）**\n  - 亚历山大·格鲁布、J·安德鲁·巴格内尔\n  - [[论文]](https:\u002F\u002Ficml.cc\u002FConferences\u002F2010\u002Fpapers\u002F451.pdf)\n\n- **利用对抗性赌盘实现快速提升（ICML 2010）**\n  - 罗伯特·布萨-费凯特、巴拉兹·凯格尔\n  - [[论文]](https:\u002F\u002Fwww.lri.fr\u002F~kegl\u002Fresearch\u002FPDFs\u002FBuKe10.pdf)\n\n- **在函数空间中利用结构信息进行提升：以图分类为例（KDD 2010）**\n  - 费宏亮、桓俊\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=1835804.1835886)\n\n- **用于提升算法的多任务学习及其在网页搜索排序中的应用（KDD 2010）**\n  - 奥利维埃·沙佩尔、潘纳加达塔·K·希瓦斯瓦米、斯里尼瓦斯·瓦德雷武、基利安·Q·温伯格、张娅、贝尔·L·曾\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=1835953)\n\n- **多分类提升算法理论（NIPS 2010）**\n  - 因德拉尼尔·穆克吉、罗伯特·E·沙皮尔\n  - [[论文]](http:\u002F\u002Frob.schapire.net\u002Fpapers\u002Fmultiboost-journal.pdf)\n\n- **提升分类器级联（NIPS 2010）**\n  - 穆罕默德·J·萨贝里安、努诺·瓦斯科塞洛斯\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4033-boosting-classifier-cascades.pdf)\n\n- **利用提升分类器的乘积进行联合级联优化（NIPS 2010）**\n  - 利奥尼达斯·莱法基斯、弗朗索瓦·弗勒雷\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F4148-joint-cascade-optimization-using-a-product-of-boosted-classifiers)\n\n- **鲁棒 LogitBoost 及自适应基础类 (ABC) LogitBoost（UAI 2010）**\n  - 李平\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1203.3491)\n  - [[代码]](https:\u002F\u002Fgithub.com\u002Fpengsun\u002FAOSOLogitBoost)\n\n## 2009年\n\n- **基于提升树的排序特征选择（CIKM 2009）**\n  - 潘峰、蒂姆·康弗斯、大卫·安、弗朗科·萨尔韦蒂、詹卢卡·多纳托\n  - [[论文]](http:\u002F\u002Fwww.francosalvetti.com\u002Fcikm09_camera2.pdf)\n\n- **利用监督式词权重方案提升KNN文本分类准确率（CIKM 2009）**\n  - 伊亚德·巴塔勒、米洛斯·豪斯克雷希特\n  - [[论文]](https:\u002F\u002Fpeople.cs.pitt.edu\u002F~milos\u002Fresearch\u002FCIKM_2009_boosting_KNN.pdf)\n  \n- **随机梯度提升的分布式决策树（CIKM 2009）**\n  - 叶杰瑞、周志恒、陈江、郑兆辉\n  - [[论文]](http:\u002F\u002Fcse.iitrpr.ac.in\u002Fckn\u002Fcourses\u002Ff2012\u002Fthomas.pdf)\n\n- **一种通用的保量级提升算法用于搜索排序（CIKM 2009）**\n  - 朱成光、陈伟祖、张泽远、王刚、王东、陈征\n  - [[论文]](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fwp-content\u002Fuploads\u002F2016\u002F06\u002Fcikm2009-1.pdf)\n  \n- **将联合提升的多类分类问题转化为邻近搜索（CVPR 2009）**\n  - 亚历山德拉·斯特凡、瓦西里斯·阿西托斯、袁泉、斯坦·斯克拉夫\n  - [[论文]](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FReducing-JointBoost-based-multiclass-classification-Stefan-Athitsos\u002F08ba1a7d91ce9b4ac26869bfe4bb7c955b0d1a24)\n\n- **不平衡RankBoost用于高效排序大规模图像视频集合（CVPR 2009）**\n  - 米凯莱·梅勒、严荣、约翰·R·史密斯\n  - [[论文]](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FImbalanced-RankBoost-for-efficiently-ranking-Merler-Yan\u002F031ba6bf0d6df8bd3aa686ce85791b7d74f0b6d5)\n\n- **正则化的多类半监督提升（CVPR 2009）**\n  - 阿米尔·萨法里、克里斯蒂安·莱斯特纳、霍斯特·比绍夫\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F5206715)\n\n- **学习关联：拥挤场景下的混合提升多目标跟踪器（CVPR 2009）**\n  - 李源、黄昌、拉姆·内瓦蒂亚\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.309.8335&rep=rep1&type=pdf)\n\n- **用于人脸验证的提升式多任务学习及其在Web图像和视频搜索中的应用（CVPR 2009）**\n  - 王晓刚、张超、张正友\n  - [[论文]](http:\u002F\u002Fwww.ee.cuhk.edu.hk\u002F~xgwang\u002Fwebface.pdf)\n\n- **LidarBoost：用于ToF 3D形状扫描的深度超分辨率（CVPR 2009）**\n  - 塞巴斯蒂安·舒恩、克里斯蒂安·泰奥巴尔特、詹姆斯·E·戴维斯、塞巴斯蒂安·瑟伦\n  - [[论文]](http:\u002F\u002Fai.stanford.edu\u002F~schuon\u002Fsr\u002Fcvpr09_poster_lidarboost.pdf)\n\n- **通过模型插值与提升实现模型自适应以优化Web搜索排名（EMNLP 2009）**\n  - 高建峰、吴强、克里斯·伯吉斯、克里斯塔·玛丽·斯沃雷、苏毅、纳赞·汗、沙林·沙赫、周红燕\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F7a82\u002F66335d0b44596574588eabb090bfeae4ab35.pdf)\n\n- **寻找可共享的信息模式及多类提升的最佳编码矩阵（ICCV 2009）**\n  - 张邦、叶给田、王洋、徐杰、赫尔曼·古纳万\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5459146)\n\n- **带有L1正则化的RankBoost用于面部表情识别与强度估计（ICCV 2009）**\n  - 杨鹏、刘庆山、迪米特里斯·N·梅塔克萨斯\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F5459371)\n\n- **基于协同训练框架的鲁棒提升跟踪器及其最小误差界（ICCV 2009）**\n  - 刘荣、程健、陆汉青\n  - [[论文]](http:\u002F\u002Fnlpr-web.ia.ac.cn\u002F2009papers\u002Fgjhy\u002Fgh1.pdf)\n\n- **教程摘要：从优化视角看提升方法综述（ICML 2009）**\n  - 曼弗雷德·K·瓦尔穆斯、S. V. N. 维什瓦纳坦\n  - [[论文]](http:\u002F\u002Fwww.stat.purdue.edu\u002F~vishy\u002Ferlpboost\u002Fmanfred.pdf)\n\n- **基分类器乘积的提升（ICML 2009）**\n  - 巴拉兹·凯格尔、罗伯特·布萨-费凯特\n  - [[论文]](https:\u002F\u002Fusers.lal.in2p3.fr\u002Fkegl\u002Fresearch\u002FPDFs\u002FkeglBusafekete09.pdf)\n\n- **ABC-boost：用于多类分类的自适应基分类器提升（ICML 2009）**\n  - 李平\n  - [[论文]](https:\u002F\u002Ficml.cc\u002FConferences\u002F2009\u002Fpapers\u002F417.pdf)\n\n- **具有结构稀疏性的提升（ICML 2009）**\n  - 约翰·C·杜奇、约拉姆·辛格\n  - [[论文]](https:\u002F\u002Fweb.stanford.edu\u002F~jduchi\u002Fprojects\u002FDuchiSi09a.pdf)\n\n- **用于鲁棒图像集目标识别的约束互子空间方法提升（IJCAI 2009）**\n  - 李曦、福井一弘、郑南宁\n  - [[论文]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F220812439_Boosting_Constrained_Mutual_Subspace_Method_for_Robust_Image-Set_Based_Object_Recognition)\n\n- **面向半监督提升的信息论正则化（KDD 2009）**\n  - 郑磊、王绍军、刘艳、李致勋\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F5255\u002F242d50851ce56354e10ae8fdcee6f47591c9.pdf)\n\n- **基于势能的不可知论提升（NIPS 2009）**\n  - 亚当·卡拉伊、瓦伦·卡纳德\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3676-potential-based-agnostic-boosting)\n\n- **带有提升的半正定度量学习（NIPS 2009）**\n  - 沈春华、金俊爱、王雷、安东·范登亨格尔\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3658-positive-semidefinite-metric-learning-with-boosting)\n\n- **带有空间正则化的提升（NIPS 2009）**\n  - 詹姆斯·向振、泰勒·向永新、乌里·哈森、彼得·J·拉马杰\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3696-boosting-with-spatial-regularization)\n  \n- **通过局部精度估计有效提升朴素贝叶斯分类器（PAKDD 2009）**\n  - 谢志鹏\n  - [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-642-01307-2_88)\n\n- **用于分类与回归问题的多分辨率提升（PAKDD 2009）**\n  - 钱丹·K·雷迪、朴仁炯\n  - [[论文]](http:\u002F\u002Fdmkd.cs.vt.edu\u002Fpapers\u002FPAKDD09.pdf)\n\n- **利用提升进行高效的主动学习（SDM 2009）**\n  - 王铮、宋阳秋、张长水\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fc8be\u002Fb70c37e4b4c4ad77e46b39060c977779d201.pdf)\n\n## 2008年\n- **基于分组的学习：一种提升方法（CIKM 2008）**\n  - 魏建尼、徐军、李航、黄亚楼\n  - [[论文]](http:\u002F\u002Fwww.bigdatalab.ac.cn\u002F~junxu\u002Fpublications\u002FCIKM2008_GroupLearn.pdf)\n\n- **利用视觉相似性学习的半监督提升方法（CVPR 2008）**\n  - 克里斯蒂安·莱斯特纳、赫尔穆特·格拉布纳、霍斯特·比绍夫\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.144.7914&rep=rep1&type=pdf)\n\n- **用于提升的组合特征挖掘（CVPR 2008）**\n  - 袁俊松、罗杰波、吴英\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?arnumber=4587347)\n\n- **用于人体对齐的增强形变模型（CVPR 2008）**\n  - 刘晓明、于婷、托马斯·塞巴斯蒂安、彼得·H·图\n  - [[论文]](https:\u002F\u002Fwww.cse.msu.edu\u002F~liuxm\u002Fpublication\u002FLiu_Yu_Sebastian_Tu_cvpr08.pdf)\n\n- **基于多级聚合的提升判别建模（CVPR 2008）**\n  - 杰森·J·科尔索\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.409.3166&rep=rep1&type=pdf)\n\n- **基于提升排序模型的人脸对齐（CVPR 2008）**\n  - 吴浩、刘晓明、詹弗兰科·多雷托\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F4587753)\n\n- **用于在线学习与跟踪的自适应线性弱分类器提升（CVPR 2008）**\n  - 托菲克·帕拉格、法蒂赫·波里克利、艾哈迈德·M·埃尔加马尔\n  - [[论文]](https:\u002F\u002Fwww.merl.com\u002Fpublications\u002Fdocs\u002FTR2008-065.pdf)\n\n- **多出口非对称提升检测（CVPR 2008）**\n  - 明-特里·范、V-D·D·黄、塔特-珍·查姆\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.330.6364&rep=rep1&type=pdf)\n\n- **用于准确快速虹膜识别的序数特征提升（CVPR 2008）**\n  - 何兆峰、孙振楠、谭铁牛、邱先超、钟成、董文博\n  - [[论文]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F224323296_Boosting_ordinal_features_for_accurate_and_fast_iris_recognition)\n\n- **通过渐进式特征组合与选择结合提升的自适应紧凑形状描述子（CVPR 2008）**\n  - 陈诚、庄宇廷、肖军、吴飞\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F4587613)\n\n- **关系序列对齐的提升（ICDM 2008）**\n  - 安德烈亚斯·卡瓦特、克里斯蒂安·克尔斯汀、尼尔斯·兰德韦尔\n  - [[论文]](https:\u002F\u002Fwww.cs.uni-potsdam.de\u002F~landwehr\u002FICDM08boosting.pdf)\n\n- **不完全信息下的提升（ICML 2008）**\n  - 戈拉姆雷扎·哈法里、王洋、王绍军、格雷格·莫里、焦峰\n  - [[论文]](http:\u002F\u002Fusers.monash.edu.au\u002F~gholamrh\u002Fpublications\u002Fboosting_icml08_slides.pdf)\n\n- **流形提升：用于全监督、半监督和无监督学习的分阶段函数逼近（ICML 2008）**\n  - 尼古拉斯·洛夫、大卫·A·福赛思、迪帕克·拉马昌德兰\n  - [[论文]](http:\u002F\u002Freason.cs.uiuc.edu\u002Fdeepak\u002Fmanifoldboost.pdf)\n\n- **随机分类噪声击败所有凸势能提升器（ICML 2008）**\n  - 菲利普·M·朗、罗科·A·塞尔维迪奥\n  - [[论文]](http:\u002F\u002Fphillong.info\u002Fpublications\u002FLS09_potential.pdf)\n\n- **具有P范数损失函数的多类代价敏感提升（KDD 2008）**\n  - 奥蕾莉·C·洛萨诺、阿部直树\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=1401953)\n\n- **MCBoost：用于图像与视觉特征感知协同聚类的多分类器提升（NIPS 2008）**\n  - 金泰均、罗伯托·西波拉\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3483-mcboost-multiple-classifier-boosting-for-perceptual-co-clustering-of-images-and-visual-features)\n\n- **PSDBoost：用于正定矩阵学习的矩阵生成线性规划（NIPS 2008）**\n  - 沈春华、艾伦·威尔士、王磊\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.879.7750&rep=rep1&type=pdf)\n\n- **关于分类损失函数的设计：理论、抗异常值能力与SavageBoost（NIPS 2008）**\n  - 哈梅德·马斯纳迪-希拉齐、努诺·瓦斯科塞洛斯\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3591-on-the-design-of-loss-functions-for-classification-theory-robustness-to-outliers-and-savageboost)\n\n- **自适应鞅提升（NIPS 2008）**\n  - 菲利普·M·朗、罗科·A·塞尔维迪奥\n  - [[论文]](http:\u002F\u002Fphillong.info\u002Fpublications\u002FLS08_adaptive_martingale_boosting.pdf)\n\n- **用于学习部分标记数据下二部排序函数的提升算法（SIGIR 2008）**\n  - 马西赫-雷扎·阿米尼、阮友荣、西里尔·古特\n  - [[论文]](http:\u002F\u002Fama.liglab.fr\u002F~amini\u002FPublis\u002FSemiSupRanking_sigir08.pdf)\n\n## 2007年\n\n- **使用纠错输出码结合模型精炼提升质心文本分类器（ACL 2007）**\n  - 谭松波\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=1557794)\n\n- **基于外观与运动的多维提升回归快速人体姿态估计（CVPR 2007）**\n  - 阿莱桑德罗·比萨科、杨明轩、斯特凡诺·索阿托\n  - [[论文]](http:\u002F\u002Fvision.ucla.edu\u002Fpapers\u002FbissaccoYS07.pdf)\n\n- **使用提升外观模型的通用人脸对齐（CVPR 2007）**\n  - 刘晓明\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?arnumber=4270290)\n\n- **Eigenboosting：结合判别与生成信息（CVPR 2007）**\n  - 赫尔穆特·格拉布纳、彼得·M·罗斯、霍斯特·比绍夫\n  - [[论文]](https:\u002F\u002Fwww.tugraz.at\u002Ffileadmin\u002Fuser_upload\u002FInstitute\u002FICG\u002FDocuments\u002Flrs\u002Fpubs\u002Fgrabner_cvpr_07.pdf)\n\n- **用于目标检测的在线学习非对称提升分类器（CVPR 2007）**\n  - 明-特里·范、塔特-珍·查姆\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F4270108)\n\n- **通过无监督在线提升改进基于部件的目标检测（CVPR 2007）**\n  - 武博、拉姆·内瓦蒂亚\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F4270173)\n\n- **适用于基于Haar-like特征的AdaBoost检测的专用处理器（CVPR 2007）**\n  - 弘本昌幸、中村健太郎、菅野弘树、中村幸宏、宫本隆介\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F4270413)\n\n- **基于局部形状特征分类器的同步目标检测与分割（CVPR 2007）**\n  - 武博、拉姆·内瓦蒂亚\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.309.9795&rep=rep1&type=pdf)\n\n- **用于计算层次化图像结构的组合提升（CVPR 2007）**\n  - 武天富、夏桂松、朱松纯\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F4270059)\n\n- **用于面部动作单元及表情识别的编码动态特征提升（CVPR 2007）**\n  - 杨鹏、刘青山、迪米特里斯·N·梅塔克萨斯\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F4270084)\n\n- **利用Adaboost进行视觉监控中的目标分类（CVPR 2007）**\n  - 约翰-保罗·雷诺、迪米特里奥斯·马克里斯、格雷厄姆·A·琼斯\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F4270512)\n\n- **用于医学解剖检测的提升回归方法（CVPR 2007）**\n  - 周少华·凯文、周景豪、多林·科马尼丘\n  - [[论文]](http:\u002F\u002Fww.w.comaniciu.net\u002FPapers\u002FBoostingRegression_CVPR07.pdf)\n\n- **基于概率提升网络的联合实时目标检测与姿态估计（CVPR 2007）**\n  - 张静丹、周少华·凯文、莱昂纳德·麦克米伦、多林·科马尼丘\n  - [[论文]](http:\u002F\u002Fcsbio.unc.edu\u002Fmcmillan\u002Fpubs\u002FCVPR07_Zhang.pdf)\n\n- **用于多类概念检测的核共享联合提升方法（CVPR 2007）**\n  - 姜伟、张世富、亚历山大·C·路易\n  - [[论文]](http:\u002F\u002Fwww.ee.columbia.edu\u002F~wjiang\u002Freferences\u002Fjiangcvprws07.pdf)\n\n- **基于尺度空间的弱回归器用于提升算法（ECML 2007）**\n  - 朴仁亨、钱丹·K·雷迪\n  - [[论文]](http:\u002F\u002Fwww.cs.wayne.edu\u002F~reddy\u002FPapers\u002FECML07.pdf)\n\n- **通过移除混淆样本避免提升算法过拟合（ECML 2007）**\n  - 亚历山大·韦日涅维茨、奥尔加·巴里诺娃\n  - [[论文]](http:\u002F\u002Fgroups.inf.ed.ac.uk\u002Fcalvin\u002Fhp_avezhnev\u002FPubs\u002FAvoidingBoostingOverfitting.pdf)\n\n- **DynamicBoost：针对动力系统生成的时间序列的提升算法（ICCV 2007）**\n  - 雷内·维达尔、保罗·法瓦罗\n  - [[论文]](http:\u002F\u002Fvision.jhu.edu\u002Fassets\u002FVidalICCV07.pdf)\n\n- **提升人脸检测器的增量学习（ICCV 2007）**\n  - 黄昌、艾海舟、山下隆义、劳世洪、川出正人\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.126.9012&rep=rep1&type=pdf)\n\n- **在线提升算法中的梯度特征选择（ICCV 2007）**\n  - 刘晓明、于婷\n  - [[论文]](https:\u002F\u002Fwww.cse.msu.edu\u002F~liuxm\u002Fpublication\u002FLiu_Yu_ICCV2007.pdf)\n\n- **基于统计信息的快速训练与Haar特征选择在提升算法人脸检测中的应用（ICCV 2007）**\n  - 范明垂、詹得珍\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.212.6173&rep=rep1&type=pdf)\n\n- **用于多视角—多姿态目标检测的聚类提升树分类器（ICCV 2007）**\n  - 吴波、拉马克特·内瓦蒂亚\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.309.9885&rep=rep1&type=pdf)\n\n- **非对称提升算法（ICML 2007）**\n  - 哈迈德·马斯纳迪-希拉齐、努诺·瓦斯科塞洛斯\n  - [[论文]](http:\u002F\u002Fwww.svcl.ucsd.edu\u002Fpublications\u002Fconference\u002F2007\u002Ficml07\u002FAsymmetricBoosting.pdf)\n\n- **用于迁移学习的提升算法（ICML 2007）**\n  - 戴文渊、杨强、薛贵荣、余勇\n  - [[论文]](http:\u002F\u002Fwww.cs.ust.hk\u002F~qyang\u002FDocs\u002F2007\u002Ftradaboost.pdf)\n\n- **用于核化输出空间的梯度提升算法（ICML 2007）**\n  - 皮埃尔·盖尔茨、路易·韦恩克尔、弗洛伦斯·达尔谢-布克\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.435.3970&rep=rep1&type=pdf)\n\n- **借助局部搜索预言机，利用提升算法寻找最大一致子集和最小不可满足子集的完整技术（IJCAI 2007）**\n  - 埃里克·格雷古瓦尔、贝尔特朗·马祖尔、塞德里克·皮埃特\n  - [[论文]](http:\u002F\u002Fwww.cril.univ-artois.fr\u002F~piette\u002FIJCAI07_HYCAM.pdf)\n\n- **使用虚拟证据提升算法训练条件随机场（IJCAI 2007）**\n  - 廖琳、坦齐姆·乔杜里、迪特·福克斯、亨利·A·考茨\n  - [[论文]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F07\u002FPapers\u002F407.pdf)\n\n- **通过结构化提升算法简单训练依存句法分析器（IJCAI 2007）**\n  - 王秦·艾丽丝、林德康、戴尔·舒尔曼斯\n  - [[论文]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F07\u002FPapers\u002F284.pdf)\n\n- **实数域上的即插即用式提升算法及其在斜决策树提升中的应用（IJCAI 2007）**\n  - 克劳迪娅·亨利、理查德·诺克、弗兰克·尼尔森\n  - [[论文]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F07\u002FPapers\u002F135.pdf)\n\n- **利用提升算法管理领域知识与多模型（IJCAI 2007）**\n  - 彭藏、查尔斯·李·伊斯贝尔二世\n  - [[论文]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F07\u002FPapers\u002F185.pdf)\n\n- **用于多标签分类的模型共享子空间提升算法（KDD 2007）**\n  - 严荣、耶莲娜·泰西奇、约翰·R·史密斯\n  - [[论文]](http:\u002F\u002Frogerioferis.com\u002FVisualRecognitionAndSearch2014\u002Fmaterial\u002Fpapers\u002FIMARSKDD2007.pdf)\n\n- **用于半监督学习的正则化提升算法（NIPS 2007）**\n  - 陈科、王世海\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3167-regularized-boost-for-semi-supervised-learning.pdf)\n\n- **用于最大化软间隔的提升算法（NIPS 2007）**\n  - 曼弗雷德·K·瓦姆思、卡伦·A·格洛瑟、冈纳尔·雷茨\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3374-boosting-algorithms-for-maximizing-the-soft-margin.pdf)\n\n- **McRank：利用多分类与梯度提升算法进行排序学习（NIPS 2007）**\n  - 李平、克里斯托弗·J·C·伯吉斯、吴强\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3270-mcrank-learning-to-rank-using-multiple-classification-and-gradient-boosting.pdf)\n\n- **单遍提升算法（NIPS 2007）**\n  - 扎费尔·巴鲁特丘卢、菲利普·M·朗、罗科·A·塞尔维迪奥\n  - [[论文]](http:\u002F\u002Fphillong.info\u002Fpublications\u002FBLS07_one_pass.pdf)\n\n- **提升ROC曲线下面积（NIPS 2007）**\n  - 菲利普·M·朗、罗科·A·塞尔维迪奥\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3247-boosting-the-area-under-the-roc-curve.pdf)\n\n- **FilterBoost：大规模数据集上的回归与分类（NIPS 2007）**\n  - 约瑟夫·K·布拉德利、罗伯特·E·沙皮尔\n  - [[论文]](http:\u002F\u002Frob.schapire.net\u002Fpapers\u002FFilterBoost_paper.pdf)\n\n- **一种通用提升方法及其在网页搜索排序函数学习中的应用（NIPS 2007）**\n  - 郑兆辉、赵宏远、张彤、奥利维尔·夏佩尔、陈可可、孙戈登\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F8f8d\u002F874a3f0217289ba317b1f6175ac3b6f73d70.pdf)\n\n- **结合主动学习的高效多分类提升算法（SDM 2007）**\n  - 黄健、赛达·埃尔特金、宋阳、赵宏远、C·李·贾伊尔斯\n  - [[论文]](https:\u002F\u002Fepubs.siam.org\u002Fdoi\u002Fabs\u002F10.1137\u002F1.9781611972771.27)\n\n- **AdaRank：用于信息检索的提升算法（SIGIR 2007）**\n  - 徐俊、李航\n  - [[论文]](http:\u002F\u002Fwww.bigdatalab.ac.cn\u002F~junxu\u002Fpublications\u002FSIGIR2007_AdaRank.pdf)\n\n## 2006年\n\n- **用于序列比对的梯度提升（AAAI 2006）**\n  - 查尔斯·帕克、艾伦·费恩、普拉萨德·塔德帕利\n  - [[论文]](http:\u002F\u002Fweb.engr.oregonstate.edu\u002F~afern\u002Fpapers\u002Faaai06-align.pdf)\n\n- **用于回归的核模型提升（ICDM 2006）**\n  - 孙平、姚新\n  - [[论文]](https:\u002F\u002Fwww.cs.bham.ac.uk\u002F~xin\u002Fpapers\u002Ficdm06SunYao.pdf)\n\n- **针对类别分布不均衡的多分类学习提升（ICDM 2006）**\n  - 孙艳敏、穆罕默德·S·卡梅尔、王洋\n  - [[论文]](http:\u002F\u002Fpeople.ee.duke.edu\u002F~lcarin\u002FImbalancedClassDistribution.pdf)\n\n- **特征空间提升：面向Web非结构化数据的文本分类（ICDM 2006）**\n  - 宋阳、周丁、黄健、伊萨克·G·科塞尔、赵洪远、C·李·贾尔斯\n  - [[论文]](http:\u002F\u002Fsonyis.me\u002Fpaperpdf\u002Ficdm06_song.pdf)\n\n- **最大化间隔的完全校正提升算法（ICML 2006）**\n  - 曼弗雷德·K·瓦姆思、廖俊、冈纳尔·雷茨施\n  - [[论文]](https:\u002F\u002Fusers.soe.ucsc.edu\u002F~manfred\u002Fpubs\u002FC75.pdf)\n\n- **提升间隔如何同时增加分类器复杂度（ICML 2006）**\n  - 列夫·雷津、罗伯特·E·沙皮尔\n  - [[论文]](http:\u002F\u002Frob.schapire.net\u002Fpapers\u002Fboost_complexity.pdf)\n\n- **基于再分区的多分类提升（ICML 2006）**\n  - 李玲\n  - [[论文]](https:\u002F\u002Fauthors.library.caltech.edu\u002F72259\u002F1\u002Fp569-li.pdf)\n\n- **AdaBoost具有一致性（NIPS 2006）**\n  - 彼得·L·巴特利特、米哈伊尔·特拉斯金\n  - [[论文]](http:\u002F\u002Fjmlr.csail.mit.edu\u002Fpapers\u002Fvolume8\u002Fbartlett07b\u002Fbartlett07b.pdf)\n\n- **用于模仿学习的结构化预测提升（NIPS 2006）**\n  - 内森·D·拉特利夫、大卫·M·布拉德利、J·安德鲁·巴格内尔、乔尔·E·切斯特纳特\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F3154-boosting-structured-prediction-for-imitation-learning.pdf)\n\n- **链式提升（NIPS 2006）**\n  - 克里斯蒂安·R·谢尔顿、韦斯利·胡伊、金飞·坎\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2981-chained-boosting)\n\n- **当高效模型平均优于提升与自助法时（PKDD 2006）**\n  - 伊恩·戴维森、范伟\n  - [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F11871637_46)\n\n## 2005年\n- **利用提升技术对室内环境进行语义位置分类的移动机器人研究（AAAI 2005）**\n  - 阿克塞尔·罗特曼、奥斯卡·马丁内斯·莫索斯、西里尔·施塔赫尼斯、沃尔夫冈·布尔加德\n  - [[论文]](http:\u002F\u002Fwww2.informatik.uni-freiburg.de\u002F~stachnis\u002Fpdf\u002Frottmann05aaai.pdf)\n\n- **基于子树特征的提升型句法分析重排序（ACL 2005）**\n  - 工藤拓、铃木淳、矶崎英树\n  - [[论文]](http:\u002F\u002Fchasen.org\u002F~taku\u002Fpublications\u002Facl2005.pdf)\n\n- **使用RankBoost比较检索系统（CIKM 2005）**\n  - 武玄庄、帕特里克·加利纳里\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.98.9470&rep=rep1&type=pdf)\n\n- **利用共享采样分布进行提升的分类器融合（ICDM 2005）**\n  - 科斯廷·巴布、拉贾·坦维尔·伊克巴尔、彭静\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F1565659)\n\n- **通过LPBoost方法实现半监督核混合模型（ICDM 2005）**\n  - 毕金波、格伦·冯、穆拉特·敦达尔、R·巴拉特·拉奥\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F1565728)\n\n- **基于增强朴素贝叶斯的高效判别式贝叶斯网络分类器学习（ICML 2005）**\n  - 井佑史、弗拉基米尔·帕夫洛维奇、詹姆斯·M·雷格\n  - [[论文]](http:\u002F\u002Fmrl.isr.uc.pt\u002Fpub\u002Fbscw.cgi\u002Fd27355\u002FJing05Efficient.pdf)\n\n- **在间隔框架下统一纠错和输出编码AdaBoost（ICML 2005）**\n  - 孙一军、西尼沙·托多罗维奇、李建、吴达鹏\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.138.4246&rep=rep1&type=pdf)\n\n- **使用概率输出编码的平滑提升算法（ICML 2005）**\n  - 金荣、张健\n  - [[论文]](http:\u002F\u002Fwww.stat.purdue.edu\u002F~jianzhan\u002Fpapers\u002Ficml05jin.pdf)\n\n- **鲁棒提升及其与自助法的关系（KDD 2005）**\n  - 萨哈龙·罗斯特\n  - [[论文]](https:\u002F\u002Fwww.tau.ac.il\u002F~saharon\u002Fpapers\u002Fbagboost.pdf)\n\n- **基于可扩展稀疏核偏最小二乘法和提升隐含特征的高效计算（KDD 2005）**\n  - 母间道成\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.387.2078&rep=rep1&type=pdf)\n\n- **用于目标检测的多实例提升（NIPS 2005）**\n  - 保罗·A·维奥拉、约翰·C·普拉特、张茶\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.138.8312&rep=rep1&type=pdf)\n\n- **具有平稳B-混合观测值的正则化提升算法的收敛性与一致性（NIPS 2005）**\n  - 奥蕾莉·C·洛萨诺、桑杰夫·R·库尔卡尼、罗伯特·E·沙皮尔\n  - [[论文]](https:\u002F\u002Fwww.cs.princeton.edu\u002F~schapire\u002Fpapers\u002Fbetamix.pdf)\n\n- **用于手写文档检索中文字识别的提升决策树（SIGIR 2005）**\n  - 尼古拉斯·R·豪、托尼·M·拉思、R·曼马塔\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.152.1551&rep=rep1&type=pdf)\n\n- **从提升中获取校准概率（UAI 2005）**\n  - 亚历山德鲁·尼库莱斯库-米齐尔、里奇·卡鲁阿纳\n  - [[论文]](https:\u002F\u002Fwww.cs.cornell.edu\u002F~caruana\u002Fniculescu.scldbst.crc.rev4.pdf)\n\n## 2004年\n\n- **在线并行提升算法（AAAI 2004）**\n  - 杰西·A·赖克勒、哈兰·D·哈里斯、迈克尔·A·萨夫琴科\n  - [[论文]](https:\u002F\u002Fwww.aaai.org\u002FPapers\u002FAAAI\u002F2004\u002FAAAI04-059.pdf)\n\n- **一种用于多示例学习的提升方法（ECML 2004）**\n  - 彼得·奥尔、罗纳德·奥特纳\n  - [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-540-30115-8_9)\n\n- **一种基于弱学习器的半结构化文本分类提升算法（EMNLP 2004）**\n  - 古藤拓、松本裕司\n  - [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FW04-3239)\n\n- **基于词和概念的弱学习器提升文本分类（ICDM 2004）**\n  - 施特凡·布洛霍恩、安德烈亚斯·霍托\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F1410303)\n\n- **利用置信度预言机进行语法推断的提升算法（ICML 2004）**\n  - 让-克里斯托夫·雅诺代、理查德·诺克、马克·塞班、亨利-马克西姆·苏希耶\n  - [[论文]](http:\u002F\u002Fwww1.univ-ag.fr\u002F~rnock\u002FArticles\u002FDrafts\u002Ficml04-jnss.pdf)\n\n- **AdaBoost与逻辑回归模型的代理最大化\u002F最小化算法（ICML 2004）**\n  - 张志华、詹姆斯·T·郭、叶迪扬\n  - [[论文]](https:\u002F\u002Ficml.cc\u002FConferences\u002F2004\u002Fproceedings\u002Fpapers\u002F77.pdf)\n\n- **通过梯度树提升训练条件随机场（ICML 2004）**\n  - 托马斯·G·迪特里希、亚当·阿申费尔特、亚罗斯拉夫·布拉托夫\n  - [[论文]](http:\u002F\u002Fweb.engr.oregonstate.edu\u002F~tgd\u002Fpublications\u002Fml2004-treecrf.pdf)\n\n- **基于间隔的距离函数提升用于聚类（ICML 2004）**\n  - 托梅尔·赫兹、阿哈龙·巴尔-希列尔、达芙娜·温沙尔\n  - [[论文]](http:\u002F\u002Fwww.cs.huji.ac.il\u002F~daphna\u002Fpapers\u002Fdistboost-icml.pdf)\n\n- **核混合的列生成提升方法（KDD 2004）**\n  - 毕进波、张彤、克里斯汀·P·贝内特\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.94.6359&rep=rep1&type=pdf)\n\n- **功能磁共振成像中分类器与提升映射的最优聚合（NIPS 2004）**\n  - 弗拉基米尔·科尔钦斯基、马内尔·马丁内斯-拉蒙、施特凡·波瑟\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2699-optimal-aggregation-of-classifiers-and-boosting-maps-in-functional-magnetic-resonance-imaging.pdf)\n\n- **流形上的提升：基分类器的自适应正则化（NIPS 2004）**\n  - 巴拉兹·凯格尔、王立根\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2613-boosting-on-manifolds-adaptive-regularization-of-base-classifiers)\n\n- **使用提升随机场的上下文模型进行目标检测（NIPS 2004）**\n  - 安东尼奥·托拉尔巴、凯文·P·墨菲、威廉·T·弗里曼\n  - [[论文]](https:\u002F\u002Fwww.cs.ubc.ca\u002F~murphyk\u002FPapers\u002FBRF-nips04-camera.pdf)\n\n- **中位数提升的泛化误差与算法收敛性（NIPS 2004）**\n  - 巴拉兹·凯格尔\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.70.8990&rep=rep1&type=pdf)\n\n- **提升在图分类中的应用（NIPS 2004）**\n  - 古藤拓、前田英作、松本裕司\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2739-an-application-of-boosting-to-graph-classification)\n\n- **有标签实例包的逻辑回归与提升（PAKDD 2004）**\n  - 徐欣、艾伯·弗兰克\n  - [[论文]](https:\u002F\u002Fwww.cs.waikato.ac.nz\u002F~ml\u002Fpublications\u002F2004\u002Fxu-frank.pdf)\n\n- **用于数据流自适应挖掘的快速轻量级提升（PAKDD 2004）**\n  - 方楚、卡洛·扎尼奥洛\n  - [[论文]](http:\u002F\u002Fweb.cs.ucla.edu\u002F~zaniolo\u002Fpapers\u002FNBCAJMW77MW0J8CP.pdf)\n\n## 2003年\n- **关于提升与指数损失（AISTATS 2003）**\n  - 亚伯拉罕·J·怀纳\n  - [[论文]](http:\u002F\u002Fwww-stat.wharton.upenn.edu\u002F~ajw\u002Fexploss.ps)\n\n- **通过无参数阈值松弛提升支持向量机进行文本分类（CIKM 2003）**\n  - 詹姆斯·G·沙纳汉、诺伯特·罗马\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=956911)\n\n- **利用提升学习跨文档结构关系（CIKM 2003）**\n  - 张竹、贾娜·奥特巴赫、德拉戈米尔·R·拉杰夫\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.128.7712&rep=rep1&type=pdf)\n\n- **关于提升改进：误差减少与收敛加速（ECML 2003）**\n  - 马克·塞班、亨利-马克西姆·苏希耶\n  - [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-540-39857-8_32)\n\n- **提升懒惰决策树（ICML 2003）**\n  - 夏莉·张·费恩、卡拉·E·布罗德利\n  - [[论文]](https:\u002F\u002Fwww.aaai.org\u002FPapers\u002FICML\u002F2003\u002FICML03-026.pdf)\n\n- **关于提升过程的收敛性（ICML 2003）**\n  - 张彤、于斌\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fdd3f\u002F901b232280533fbdb9e57f144f44723617cf.pdf)\n\n- **针对不均衡数据集的线性规划提升（ICML 2003）**\n  - 尤雷·莱斯科韦茨、约翰·肖伊-泰勒\n  - [[论文]](https:\u002F\u002Fcs.stanford.edu\u002Fpeople\u002Fjure\u002Fpubs\u002Ftextbooster-icml03.pdf)\n\n- **蒙特卡洛理论对自助法与提升的解释（IJCAI 2003）**\n  - 罗伯托·埃斯波西托、洛伦扎·赛塔\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=1630733)\n\n- **关于提升的动力学（NIPS 2003）**\n  - 辛西娅·鲁丁、英格丽·道布奇、罗伯特·E·沙皮尔\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2535-on-the-dynamics-of-boosting)\n\n- **用于上下文推理的相互提升（NIPS 2003）**\n  - 迈克尔·芬克、皮耶特罗·佩罗纳\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2520-mutual-boosting-for-contextual-inference)\n\n- **提升与覆盖之间的对比（NIPS 2003）**\n  - 桥本浩平、曼弗雷德·K·瓦穆斯\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2532-boosting-versus-covering)\n\n- **通过析取规划提升进行多示例学习（NIPS 2003）**\n  - 斯图尔特·安德鲁斯、托马斯·霍夫曼\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2478-multiple-instance-learning-via-disjunctive-programming-boosting)\n\n- **平均提升：一种抗噪集成方法（PAKDD 2003）**\n  - 金永大\n  - [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-36175-8_38)\n\n- **SMOTEBoost：提升中少数类预测的改进（PKDD 2003）**\n  - 尼特什·V·乔拉、亚历山大·拉扎列维奇、劳伦斯·O·霍尔、凯文·W·鲍耶\n  - [[论文]](https:\u002F\u002Fwww3.nd.edu\u002F~nchawla\u002Fpapers\u002FECML03.pdf)\n\n## 2002年\n\n- **最小多数分类与提升算法（AAAI 2002）**\n  - 菲利普·M·朗\n  - [[论文]](http:\u002F\u002Fphillong.info\u002Fpublications\u002Fminmaj.pdf)\n\n- **命名实体抽取的排序算法：提升与投票感知机（ACL 2002）**\n  - 迈克尔·柯林斯\n  - [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP02-1062)\n\n- **通过提升纠正文本分类中的归纳偏差（CIKM 2002）**\n  - 刘燕、杨一鸣、海梅·G·卡波内尔\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=584792.584850)\n  \n- **仅修改一行代码，如何使AdaBoost.M1适用于弱基分类器（ECML 2002）**\n  - 冈瑟·艾布尔、卡尔·彼得·普费弗\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=650068)\n\n- **基于间隔的特征与关系选择实现提升算法的扩展性（ECML 2002）**\n  - 苏珊娜·霍赫、斯特凡·沃贝尔\n  - [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-36755-1_13)\n\n- **一种鲁棒的提升算法（ECML 2002）**\n  - 理查德·诺克、帕特里斯·勒福谢尔\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=650081)\n\n- **iBoost：基于实例的指数加权方案的提升算法（ECML 2002）**\n  - 斯蒂芬·奎克、周阮\n  - [[论文]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F220516082_iBoost_Boosting_using_an_instance-based_exponential_weighting_scheme)\n\n- **密度函数估计器的提升（ECML 2002）**\n  - 弗兰克·托拉尔、马克·塞班、菲利普·埃泽凯尔\n  - [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F3-540-36755-1_36)\n  \n- **支持向量机、提升及其他方法的统计行为与一致性（ICML 2002）**\n  - 张彤\n  - [[论文]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F221344927_Statistical_Behavior_and_Consistency_of_Support_Vector_Machines_Boosting_and_Beyond)\n\n- **用于学习文本分块的提升最大熵模型（ICML 2002）**\n  - 朴成培、张炳卓\n  - [[论文]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F221345636_A_Boosted_Maximum_Entropy_Model_for_Learning_Text_Chunking)\n\n- **通过提升与判别式训练构建大间隔语音识别器（ICML 2002）**\n  - 卡斯滕·迈耶、彼得·拜耶莱因\n  - [[论文]](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTowards-Large-Margin-Speech-Recognizers-by-Boosting-Meyer-Beyerlein\u002F8408479e36da812cdbf6bc15f7849c3e76a1016d)\n\n- **将先验知识融入提升算法（ICML 2002）**\n  - 罗伯特·E·沙皮尔、玛丽·罗谢里、马津·G·拉希姆、纳伦德拉·K·古普塔\n  - [[论文]](http:\u002F\u002Frob.schapire.net\u002Fpapers\u002Fboostknowledge.pdf)\n\n- **基于提升的条件密度估计建模拍卖价格不确定性（ICML 2002）**\n  - 罗伯特·E·沙皮尔、彼得·斯通、戴维·A·麦卡莱斯特、迈克尔·L·利特曼、雅诺什·A·齐里克\n  - [[论文]](http:\u002F\u002Fwww.cs.utexas.edu\u002F~ai-lab\u002Fpubs\u002FICML02-tac.pdf)\n\n- **MARK：用于异构核模型的提升算法（KDD 2002）**\n  - 克里斯汀·P·本内特、米奇纳里·蒙马、马克·J·恩布雷茨\n  - [[论文]](http:\u002F\u002Fhomepages.rpiscrews.us\u002F~bennek\u002Fpapers\u002Fkdd2.pdf)\n\n- **预测稀有类别：提升能否使任何弱学习器变强（KDD 2002）**\n  - 马赫什·V·乔希、拉梅什·C·阿加瓦尔、维平·库马尔\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.13.1159&rep=rep1&type=pdf)\n\n- **利用提升设计核函数（NIPS 2002）**\n  - 科比·克拉默、约瑟夫·凯舍特、约拉姆·辛格\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fff79\u002F344807e972fdd7e5e1c3ed5c539dd1aeecbe.pdf)\n\n- **用于分类的FloatBoost学习（NIPS 2002）**\n  - 斯坦·Z·李、张振秋、沈弘扬、张洪江\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F8ccc\u002F5ef87eab96a4cae226750eba8322b30606ea.pdf)\n\n- **通过提升进行标签序列的判别式学习（NIPS 2002）**\n  - 亚塞敏·阿尔顿、托马斯·霍夫曼、马克·约翰逊\n  - [[论文]](http:\u002F\u002Fweb.science.mq.edu.au\u002F~mjohnson\u002Fpapers\u002Fnips02.pdf)\n\n- **密度估计的提升（NIPS 2002）**\n  - 萨哈龙·罗塞特、埃兰·塞格尔\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2298-boosting-density-estimation.pdf)\n\n- **自监督提升（NIPS 2002）**\n  - 马克斯·韦林、理查德·S·泽梅尔、杰弗里·E·欣顿\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F6a2a\u002Ff112a803e70c23b7055de2e73007cf42c301.pdf)\n\n- **提升二元核判别式（NIPS 2002）**\n  - 巴巴克·莫加达姆、格雷戈里·沙赫纳罗维奇\n  - [[论文]](http:\u002F\u002Fwww.merl.com\u002Fpublications\u002Fdocs\u002FTR2002-55.pdf)\n  \n- **一种提升支持向量机的方法（PAKDD 2002）**\n  - 迪奥莉莉、胡克云、陆宇昌、施春义\n  - [[论文]](https:\u002F\u002Felkingarcia.github.io\u002FPapers\u002FMLDM07.pdf)\n\n- **一种提升朴素贝叶斯分类器的方法（PAKDD 2002）**\n  - 迪奥莉莉、胡克云、陆宇昌、施春义\n  - [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-47887-6_11)\n\n- **预测稀有类别：两阶段规则归纳与代价敏感提升的比较（PKDD 2002）**\n  - 马赫什·V·乔希、拉梅什·C·阿加瓦尔、维平·库马尔\n  - [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-45681-3_20)\n\n- **基于分布敏感距离的迭代数据压缩用于提升（PKDD 2002）**\n  - 椿木悠太、铃木荣信\n  - [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-45681-3_8)\n\n- **分阶段混合建模与提升（UAI 2002）**\n  - 克里斯托弗·米克、博·蒂森、大卫·赫克曼\n  - [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1301.0586)\n\n- **提升算法的进展（UAI 2002）**\n  - 罗伯特·E·沙皮尔\n  - [[论文]](http:\u002F\u002Frob.schapire.net\u002Fpapers\u002Fuai02.pdf)\n\n## 2001年\n- **提升算法是否需要正则化？（AISTATS 2001）**\n  - 江文欣\n  - [[论文]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F2439718_Is_Regularization_Unnecessary_for_Boosting)\n\n- **在线自助法与提升法（AISTATS 2001）**\n  - Nikunj C. Oza, Stuart J. Russell\n  - [[论文]](https:\u002F\u002Fti.arc.nasa.gov\u002Fm\u002Fprofile\u002Foza\u002Ffiles\u002Fozru01a.pdf)\n  \n- **基于直推式提升的文本分类（ECML 2001）**\n  - Hirotoshi Taira, Masahiko Haruno\n  - [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-44795-4_39)\n\n- **利用提升结合系统改进术语提取（ECML 2001）**\n  - Jordi Vivaldi, Lluís Màrquez, Horacio Rodríguez\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3108351)\n\n- **AdaBoost.M2在模拟数字识别任务中的性能分析（ECML 2001）**\n  - Günther Eibl, Karl Peter Pfeiffer\n  - [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-44795-4_10)\n\n- **分支程序提升的实际应用研究（ECML 2001）**\n  - Tapio Elomaa, Matti Kääriäinen\n  - [[论文]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F221112522_On_the_Practice_of_Branching_Program_Boosting)\n\n- **用于半监督学习的混合模型提升（ICANN 2001）**\n  - Yves Grandvalet, Florence d'Alché-Buc, Christophe Ambroise\n  - [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-44668-0_7)\n\n- **堆叠结合元决策树与自助法、提升法及其他方法的比较（ICDM 2001）**\n  - Bernard Zenko, Ljupco Todorovski, Saso Dzeroski\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.23.3118&rep=rep1&type=pdf)\n\n- **利用提升简化分类模型（ICDM 2001）**\n  - Virginia Wheway\n  - [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F989565)\n\n- **评估提升算法对稀有类别的分类：比较与改进（ICDM 2001）**\n  - Mahesh V. Joshi, Vipin Kumar, Ramesh C. Agarwal\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fb829\u002Ffe743e4beeeed65d32d2d7931354df7a2f60.pdf)\n  - [[代码]]( )\n\n- **基于邻域的分类器提升（ICML 2001）**\n  - Marc Sebban, Richard Nock, Stéphane Lallich\n  - [[论文]](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FBoosting-Neighborhood-Based-Classifiers-Sebban-Nock\u002Fee88e3bbe8a7e81cae7ee53da2c824de7c82f882)\n\n- **噪声数据下的提升算法（ICML 2001）**\n  - Abba Krieger, Chuan Long, Abraham J. Wyner\n  - [[论文]](https:\u002F\u002Fwww.researchgate.net\u002Fprofile\u002FAbba_Krieger\u002Fpublication\u002F221345435_Boosting_Noisy_Data\u002Flinks\u002F00463528a1ba641692000000.pdf)\n\n- **噪声数据环境下提升算法的一些理论问题（ICML 2001）**\n  - 江文欣\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload;jsessionid=2494A2C06ACA22FA971AC1C29B53FF62?doi=10.1.1.27.7231&rep=rep1&type=pdf)\n\n- **特征选择中的过滤法、包装法及基于提升的混合方法（ICML 2001）**\n  - Sanmay Das\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F93b6\u002F25a0e35b59fa6a3e7dc1cbdb31268d62d69f.pdf)\n\n- **分布式提升算法（KDD 2001）**\n  - Aleksandar Lazarevic, Zoran Obradovic\n  - [[论文]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F2488971_The_Distributed_Boosting_Algorithm)\n\n- **在线与批处理版本的自助法和提升法的实验比较（KDD 2001）**\n  - Nikunj C. Oza, Stuart J. Russell\n  - [[论文]](https:\u002F\u002Fpeople.eecs.berkeley.edu\u002F~russell\u002Fpapers\u002Fkdd01-online.pdf)\n\n- **半监督的间隔提升（NIPS 2001）**\n  - Florence d'Alché-Buc, Yves Grandvalet, Christophe Ambroise\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F2197\u002Ff1c2d55827b6928cc80030922569acce2d6c.pdf)\n\n- **指数族模型的提升与最大似然估计（NIPS 2001）**\n  - Guy Lebanon, John D. Lafferty\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2042-boosting-and-maximum-likelihood-for-exponential-models.pdf)\n\n- **利用非对称AdaBoost和检测器级联实现快速且鲁棒的分类（NIPS 2001）**\n  - Paul A. Viola, Michael J. Jones\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.68.4306&rep=rep1&type=pdf)\n  \n- **异构数据库中局部化分类器的提升（SDM 2001）**\n  - Aleksandar Lazarevic, Zoran Obradovic\n  - [[论文]](https:\u002F\u002Fepubs.siam.org\u002Fdoi\u002Fabs\u002F10.1137\u002F1.9781611972719.14)\n\n- **贪婪函数逼近：梯度提升机（Ann. Statist 2001）**\n  - Jerome H. Friedman\n  - [[论文]](https:\u002F\u002Fprojecteuclid.org\u002Fjournals\u002Fannals-of-statistics\u002Fvolume-29\u002Fissue-5\u002FGreedy-function-approximation-A-gradient-boosting-machine\u002F10.1214\u002Faos\u002F1013203451.full)\n\n## 2000年\n- **增强包装归纳法（AAAI 2000）**\n  - 戴恩·弗里塔格、尼古拉斯·库什梅里克\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fd009\u002Fa2bd48a9d1971fbc0d99f6df00539a62048a.pdf)\n\n- **一种改进的提升算法及其在文本分类中的应用（CIKM 2000）**\n  - 法布里齐奥·塞巴斯蒂亚尼、亚历山德罗·斯佩尔杜蒂、尼古拉·瓦尔丹布里尼\n  - [[论文]](http:\u002F\u002Fnmis.isti.cnr.it\u002Fsebastiani\u002FPublications\u002FCIKM00.pdf)\n\n- **用于文档路由的提升方法（CIKM 2000）**\n  - 拉杰·D·艾耶尔、大卫·D·刘易斯、罗伯特·E·沙皮尔、约拉姆·辛格、阿米特·辛格\n  - [[论文]](http:\u002F\u002Fsinghal.info\u002Fcikm-2000.pdf)\n\n- **关于提升剪枝问题的研究（ECML 2000）**\n  - 克里斯蒂诺·塔蒙、向杰\n  - [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-45164-1_41)\n\n- **提升算法在词义消歧中的应用（ECML 2000）**\n  - 杰拉德·埃斯库德罗、略伊士·马尔克斯、赫尔曼·里高\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=649539)\n\n- **基于提升算法的MetaCost方法的实证研究（ECML 2000）**\n  - 凯明·丁\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.218.1624&rep=rep1&type=pdf)\n\n- **FeatureBoost：一种提高模型鲁棒性的元学习算法（ICML 2000）**\n  - 约瑟夫·奥沙利文、约翰·兰福德、里奇·卡鲁纳、阿夫里姆·布卢姆\n  - [[论文]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F221345746_FeatureBoost_A_Meta-Learning_Algorithm_that_Improves_Model_Robustness)\n\n- **最小描述长度原则与提升算法在话语自动分析中的比较（ICML 2000）**\n  - 野本忠、松本裕二\n  - [[论文]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F221344998_Comparing_the_Minimum_Description_Length_Principle_and_Boosting_in_the_Automatic_Analysis_of_Discourse)\n\n- **基于提升的子对话主题检测方法（ICML 2000）**\n  - 卡里·迈尔斯、迈克尔·J·科尔恩斯、萨廷德尔·P·辛格、玛丽琳·A·沃克\n  - [[论文]](https:\u002F\u002Fwww.cis.upenn.edu\u002F~mkearns\u002Fpapers\u002Ftopicspot.pdf)\n\n- **代价敏感提升算法的比较研究（ICML 2000）**\n  - 凯明·丁\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=657944)\n\n- **对仅使用正例数据的学习器进行提升（ICML 2000）**\n  - 安德鲁·R·米切尔\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fsummary?doi=10.1.1.34.3669)\n\n- **用于提升的列生成算法（ICML 2000）**\n  - 克里斯汀·P·贝内特、艾汉·德米里兹、约翰·肖伊-泰勒\n  - [[论文]](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload;jsessionid=1828D5853F656BD6892E9C2C446ECC68?doi=10.1.1.16.9612&rep=rep1&type=pdf)\n\n- **基于梯度的回归问题提升算法（NIPS 2000）**\n  - 理查德·S·泽梅尔、托尼安·皮塔西\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fc41a\u002F9417f5605b55bdd216d119e47669a92f5c50.pdf)\n\n- **弱学习器与提升算法收敛速度的改进（NIPS 2000）**\n  - 希·曼诺尔、罗恩·梅尔\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F1906-weak-learners-and-improved-rates-of-convergence-in-boosting.pdf)\n\n- **针对具有不稳定驱动属性的空间函数的自适应提升方法（PAKDD 2000）**\n  - 亚历山大·拉扎雷维奇、蒂姆·菲兹、佐兰·奥布拉多维奇\n  - [[论文]](http:\u002F\u002Fwww.dabi.temple.edu\u002F~zoran\u002Fpapers\u002Flazarevic01j.pdf)\n\n- **通过自适应采样扩展基于提升的学习器规模（PAKDD 2000）**\n  - 卡洛斯·多明戈、渡边修\n  - [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-45571-X_37)\n\n- **一阶逻辑时间序列分类器的学习：规则与提升（PKDD 2000）**\n  - 胡安·J·罗德里格斯·迪埃斯、卡洛斯·阿隆索·冈萨雷斯、亨里克·博斯特伦\n  - [[论文]](https:\u002F\u002Fpeople.dsv.su.se\u002F~henke\u002Fpapers\u002Frodriguez00b.pdf)\n\n- **带有动态集成机制的装袋与提升方法（PKDD 2000）**\n  - 阿列克谢·琴巴尔、塞波·普乌罗宁\n  - [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F3-540-45372-5_12)\n\n- **通过提升朴素贝叶斯分类器进行文本过滤（SIGIR 2000）**\n  - 金宇焕、韩尚润、张炳卓\n  - [[论文]](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F221299823_Text_filtering_by_boosting_Naive_Bayes_classifiers)\n\n## 1999年\n- **用于回归问题的提升方法论（AISTATS 1999）**\n  - 格雷格·里奇威、大卫·麦迪根、托马斯·理查德森\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F5f19\u002F6a8baa281b2190c4519305bec8f5c91c8e5a.pdf)\n\n- **提升算法在词性标注及介词短语依附关系分析中的应用（EMNLP 1999）**\n  - 史蒂文·阿布尼、罗伯特·E·沙皮尔、约拉姆·辛格\n  - [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FW99-0606)\n\n- **懒惰贝叶斯规则：一种与提升决策树相竞争的懒惰半朴素贝叶斯学习技术（ICML 1999）**\n  - 齐建·郑、杰弗里·I·韦伯、凯明·丁\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F067e\u002F86836ddbcb5e2844e955c16e058366a18c77.pdf)\n\n- **AdaCost：误分类代价敏感提升算法（ICML 1999）**\n  - 魏凡、萨尔瓦托雷·J·斯托尔福、张俊欣、菲利普·K·陈\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F9ddf\u002Fbc2cc5c1b13b80a1a487b9caa57e80edd863.pdf)\n\n- **对强学习器进行提升：反对最小间隔假设的证据（ICML 1999）**\n  - 迈克尔·邦内尔·哈里斯\n  - [[论文]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=657480)\n\n- **作为梯度下降的提升算法（NIPS 1999）**\n  - 列夫·梅森、乔纳森·巴克ستر、彼得·L·巴特利特、马库斯·R·弗里恩\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F1766-boosting-algorithms-as-gradient-descent.pdf)\n\n- **决策树中多路分支的提升方法（NIPS 1999）**\n  - 伊夏伊·曼苏尔、戴维·A·麦克阿莱斯特\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F1659-boosting-with-multi-way-branching-in-decision-trees.pdf)\n\n- **潜在的提升算子（NIPS 1999）**\n  - 尼格尔·达菲、戴维·P·海尔博尔德\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F4884\u002Fc765b6ceab7bdfb6703489810c8a386fd2a8.pdf)\n\n## 1998年\n- **一种高效的组合偏好信息的提升算法（ICML 1998）**\n  - 约阿夫·弗伦德、拉杰·D·艾耶尔、罗伯特·E·沙皮尔、约拉姆·辛格\n  - [[论文]](http:\u002F\u002Fjmlr.csail.mit.edu\u002Fpapers\u002Fvolume4\u002Ffreund03a\u002Ffreund03a.pdf)\n\n- **利用提升与装袋的查询学习策略（ICML 1998）**\n  - 直木直树、间冢浩史\n  - [[论文]](https:\u002F\u002Fwww.bic.kyoto-u.ac.jp\u002Fpathway\u002Fmami\u002Fpubs\u002FFiles\u002Ficml98.pdf)\n\n- **AdaBoost的正则化（NIPS 1998）**\n  - 冈纳尔·雷茨施、小野隆志、克劳斯-罗伯特·穆勒\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F0afc\u002F9de245547c675d40ad29240e2788c0416f91.pdf)\n\n## 1997年\n- **提升分类间隔：对投票方法有效性的新解释（ICML 1997）**\n  - 罗伯特·E·沙皮尔、约阿夫·弗罗因德、彼得·巴雷特、李伟森\n  - [[论文]](https:\u002F\u002Fwww.cc.gatech.edu\u002F~isbell\u002Ftutorials\u002Fboostingmargins.pdf)\n\n- **利用输出编码提升多分类学习问题（ICML 1997）**\n  - 罗伯特·E·沙皮尔\n  - [[论文]](http:\u002F\u002Frob.schapire.net\u002Fpapers\u002FSchapire97.pdf)\n\n- **使用提升技术改进回归模型（ICML 1997）**\n  - 哈里斯·德拉克尔\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002F8d49\u002Fe2dedb817f2c3330e74b63c5fc86d2399ce3.pdf)\n\n- **自适应提升算法的剪枝（ICML 1997）**\n  - 德拉戈斯·D·马尔吉内安图、托马斯·G·迪特里希\n  - [[论文]](https:\u002F\u002Fpdfs.semanticscholar.org\u002Fb25f\u002F615fc139fbdeccc3bcf4462f908d7f8e37f9.pdf)\n\n- **神经网络自适应提升的训练方法（NIPS 1997）**\n  - 霍尔格·施文克、约书亚·本吉奥\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F1335-training-methods-for-adaptive-boosting-of-neural-networks.pdf)\n\n## 1996年\n- **一种新型提升算法的实验（ICML 1996）**\n  - 相关作者未明确列出，但可能与约阿夫·弗罗因德和罗伯特·E·沙皮尔有关\n  - [[论文]](https:\u002F\u002Fcseweb.ucsd.edu\u002F~yfreund\u002Fpapers\u002Fboostingexperiments.pdf)\n\n## 1995年\n- **提升决策树（NIPS 1995）**\n  - 哈里斯·德拉克尔、科琳娜·科特斯\n  - [[论文]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F1059-boosting-decision-trees.pdf)\n\n## 1994年\n- **提升及其他机器学习算法（ICML 1994）**\n  - 哈里斯·德拉克尔、科琳娜·科特斯、劳伦斯·D·杰克尔、扬·勒丘恩、弗拉基米尔·瓦普尼克\n  - [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FB9781558603356500155)\n\n--------------------------------------------------------------------------------\n\n**许可**\n\n- [CC0 Universal](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-gradient-boosting-papers\u002Fblob\u002Fmaster\u002FLICENSE)","# awesome-gradient-boosting-papers 快速上手指南\n\n`awesome-gradient-boosting-papers` 不是一个可直接安装的软件库或工具包，而是一个**精选的梯度提升（Gradient Boosting）相关研究论文与代码实现的清单仓库**。本指南将帮助你快速浏览、检索并利用该资源中的高质量学术成果。\n\n## 环境准备\n\n由于本项目本质是文档索引，无需特定的运行时环境。你只需要：\n\n- **操作系统**：Windows \u002F macOS \u002F Linux 均可\n- **前置依赖**：\n  - Git（用于克隆仓库）\n  - 现代浏览器（用于在线阅读）\n  - Python 3.x（可选，用于运行列表中链接的具体代码实现）\n\n## 安装步骤\n\n你可以通过以下两种方式获取该资源列表：\n\n### 方式一：在线浏览（推荐）\n直接访问 GitHub 仓库页面查看最新整理的论文列表：\n```text\nhttps:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-gradient-boosting-papers\n```\n\n### 方式二：本地克隆\n如果你希望离线阅读或贡献内容，请使用 Git 克隆仓库：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-gradient-boosting-papers.git\ncd awesome-gradient-boosting-papers\n```\n\n> **国内加速提示**：如果访问 GitHub 速度较慢，可使用国内镜像源克隆（需确保镜像源同步正常）：\n> ```bash\n> git clone https:\u002F\u002Fgitee.com\u002Fmirrors\u002Fawesome-gradient-boosting-papers.git\n> ```\n> *注：若上述镜像不可用，请配置本地 Git 代理或使用其他加速服务。*\n\n## 基本使用\n\n本仓库的核心价值在于其分类清晰的论文索引。以下是使用流程：\n\n1. **按年份或领域查找论文**\n   打开 `README.md` 文件，内容已按年份（2025, 2024, ...）和会议领域（如 NeurIPS, KDD, ICLR 等）分类。例如，查找 2024 年关于公平性的研究：\n   - 定位到 `## 2024` 或 `## 2023` 章节。\n   - 寻找标题含 \"Fair\" 的条目，如 `FairGBM: Gradient Boosting with Fairness Constraints (ICLR 2023)`。\n\n2. **获取论文与代码**\n   每个条目都提供了直接链接：\n   - 点击 `[[Paper]]` 链接阅读 arXiv 或会议官方论文。\n   - 点击 `[[Code]]` 链接跳转至对应的 GitHub 实现仓库。\n\n3. **运行具体算法示例**\n   假设你对 **NRGBoost (ICLR 2025)** 感兴趣：\n   - 点击该条目的 `[[Code]]` 链接进入 `https:\u002F\u002Fgithub.com\u002Fajoo\u002Fnrgboost`。\n   - 按照该子项目的 README 指示安装依赖并运行：\n     ```bash\n     # 示例：进入具体项目目录后的典型操作\n     pip install -r requirements.txt\n     python train.py --config config\u002Fnrgboost.yaml\n     ```\n\n4. **追踪最新动态**\n   该项目持续更新，建议定期 Pull 最新内容或关注作者 Twitter (@benrozemberczki) 获取最新论文收录通知：\n   ```bash\n   cd awesome-gradient-boosting-papers\n   git pull origin master\n   ```\n\n通过此清单，你可以高效地找到带有开源代码实现的最新梯度提升算法，直接复现或应用于你的机器学习项目中。","某金融科技公司风控团队正致力于升级反欺诈模型，急需引入最新的梯度提升算法来处理复杂的非平衡数据并满足隐私合规要求。\n\n### 没有 awesome-gradient-boosting-papers 时\n- **文献检索如大海捞针**：研究人员需在 NeurIPS、KDD 等数十个顶级会议的海量论文中手动筛选，耗时数周仍可能遗漏关键突破（如联邦学习在欺诈检测中的应用）。\n- **复现门槛极高**：找到论文后，往往发现官方未开源代码或实现框架不匹配，团队需从零重写算法，导致项目进度严重滞后。\n- **技术选型盲目**：缺乏对最新变体（如针对区间删失数据的 Boosting 方法）的系统认知，只能沿用旧版 XGBoost\u002FLightGBM，难以解决特定业务痛点。\n- **跨领域洞察缺失**：难以发现计算机视觉或 NLP 领域中可迁移至风控场景的创新 boosting 架构，限制了模型性能的上限。\n\n### 使用 awesome-gradient-boosting-papers 后\n- **精准锁定前沿成果**：团队直接查阅列表中\"CIKM 2025\"分类，迅速定位到《Federated Gradient Boosting for Financial Fraud Detection》等高度匹配的论文与代码链接。\n- **开箱即用的基线**：利用仓库提供的已验证实现（如 `fairregboost`），当天即可搭建公平性回归基线，将原本数周的预研工作压缩至数天。\n- **针对性解决难题**：基于列表中发现的\"Interval-censored Data\"相关研究，快速引入新算法处理用户行为数据中的时间不确定性问题，显著提升召回率。\n- **激发跨界创新**：通过浏览不同领域的收录论文，团队成功将图分类中的增强思路迁移至交易网络分析，构建了更具解释性的风控特征。\n\nawesome-gradient-boosting-papers 将分散的学术瑰宝转化为触手可及的工程利器，让算法团队从繁琐的搜寻工作中解放，专注于核心业务价值的创造。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fbenedekrozemberczki_awesome-gradient-boosting-papers_06d50720.gif","benedekrozemberczki","Benedek Rozemberczki","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fbenedekrozemberczki_4cc882ba.png","Machine Learning Research Scientist at Google| PhD from The University of Edinburgh.","@google","United Kingdom","benedek.rozemberczki@gmail.com","benrozemberczki",null,"https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki",[83],{"name":84,"color":85,"percentage":86},"Python","#3572A5",100,1050,164,"2026-04-10T04:55:57","CC0-1.0",1,"","未说明",{"notes":95,"python":93,"dependencies":96},"该仓库是一个关于梯度提升（Gradient Boosting）研究论文的精选列表，本身不是一个可运行的软件工具或代码库，因此没有特定的运行环境、依赖库或硬件需求。列表中提到的各个论文项目拥有独立的代码仓库和各自的环境要求，需参考具体论文链接中的代码实现。",[],[14],[99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118],"gradient-boosting","gradient-boosting-classifier","gradient-boosting-machine","gradient-boosted-trees","gradient-boosting-decision-trees","xgboost","xgboost-algorithm","catboost","lightgbm","random-forest","decision-tree","classification-algorithm","classification-trees","machine-learning","deep-learning","h2o","classifier","classification-tree","adaboost","boosting","2026-03-27T02:49:30.150509","2026-04-20T04:04:32.497362",[],[123,128,133,138],{"id":124,"version":125,"summary_zh":126,"released_at":127},343039,"v_0004","包含2023年、2024年和2025年主要会议中的梯度提升相关论文。","2026-01-05T12:39:45",{"id":129,"version":130,"summary_zh":131,"released_at":132},343040,"v_0003","- 新增了CIKM 2021的论文","2021-11-27T11:41:10",{"id":134,"version":135,"summary_zh":136,"released_at":137},343041,"v_0002","已更新来自以下会议的相关论文：\n\nICML 2021、ICLR 2021、AAAI 2021","2021-07-25T15:48:05",{"id":139,"version":140,"summary_zh":80,"released_at":141},343042,"v_0001","2021-05-04T21:28:04"]