“用于可解释机器学习模型开发与诊断的 PiML 工具箱”（A. Sudjianto、A. Zhang、Z. Yang、Y. Su 和 N. Zeng，2023 年） arXiv 链接

@article{sudjianto2023piml,
title={PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics},
author={Sudjianto, Agus and Zhang, Aijun and Yang, Zebin and Su, Yu and Zeng, Ningzhou},
year={2023}
}

“设计内在可解释的机器学习模型”（A. Sudjianto 和 A. Zhang，2021 年） arXiv 链接

@article{sudjianto2021designing,
title={Designing Inherently Interpretable Machine Learning Models},
author={Sudjianto, Agus and Zhang, Aijun},
journal={arXiv preprint:2111.01743},
year={2021}
}

“揭开深度 ReLU 网络的黑箱：可解释性、诊断与简化”（A. Sudjianto、W. Knauth、R. Singh、Z. Yang 和 A. Zhang，2020 年） arXiv 链接

@article{sudjianto2020unwrapping,
title={Unwrapping the black box of deep ReLU networks: interpretability, diagnostics, and simplification},
author={Sudjianto, Agus and Knauth, William and Singh, Rahul and Yang, Zebin and Zhang, Aijun},
journal={arXiv preprint:2011.04041},
year={2020}
}

“GAMI-Net：基于具有结构化交互作用的广义加性模型的可解释神经网络”（Z. Yang、A. Zhang 和 A. Sudjianto，2021 年） arXiv 链接

@article{yang2021gami,
title={GAMI-Net: An explainable neural network based on generalized additive models with structured interactions},
author={Yang, Zebin and Zhang, Aijun and Sudjianto, Agus},
journal={Pattern Recognition},
volume={120},
pages={108192},
year={2021}
}

“快速可解释的贪婪树求和模型 (FIGS)”（Tan, Y.S., Singh, C., Nasseri, K., Agarwal, A. 和 Yu, B., 2022 年）

@article{tan2022fast,
title={Fast interpretable greedy-tree sums (FIGS)},
author={Tan, Yan Shuo and Singh, Chandan and Nasseri, Keyan and Agarwal, Abhineet and Yu, Bin},
journal={arXiv preprint arXiv:2201.11931},
year={2022}
}

“具有成对交互作用的准确且易懂的模型”（Y. Lou、R. Caruana、J. Gehrke 和 G. Hooker，2013 年）

@inproceedings{lou2013accurate,
title={Accurate intelligible models with pairwise interactions},
author={Lou, Yin and Caruana, Rich and Gehrke, Johannes and Hooker, Giles},
booktitle={Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages={623--631},
year={2013},
organization={ACM}
}  

“利用函数型方差分析净化交互效应：一种恢复可识别加性模型的有效算法”（Lengerich, B., Tan, S., Chang, C.H., Hooker, G. 和 Caruana, R., 2020 年）

@inproceedings{lengerich2020purifying,
title={Purifying interaction effects with the functional anova: An efficient algorithm for recovering identifiable additive models},
author={Lengerich, Benjamin and Tan, Sarah and Chang, Chun-Hao and Hooker, Giles and Caruana, Rich},
booktitle={International Conference on Artificial Intelligence and Statistics},
pages={2402--2412},
year={2020},
organization={PMLR}
}

“InterpretML：机器学习可解释性的统一框架”（H. Nori、S. Jenkins、P. Koch 和 R. Caruana，2019 年）

@article{nori2019interpretml,
title={InterpretML: A Unified Framework for Machine Learning Interpretability},
author={Nori, Harsha and Jenkins, Samuel and Koch, Paul and Caruana, Rich},
journal={arXiv preprint:1909.09223},
year={2019}
}