A simple illustration to run RKHS-SHAP, GP-SHAP, and BayesGPSHAP to explain kernel methods and Gaussian process.
The algorithms used in this repo came primarily out of the following papers. If you use RKHS-SHAP or GP-SHAP in your research we would appreciate a citation to the appropriate paper(s):
@article{chau2022rkhs,
title={RKHS-SHAP: Shapley values for kernel methods},
author={Chau, Siu Lun and Hu, Robert and Gonzalez, Javier and Sejdinovic, Dino},
journal={Advances in Neural Information Processing Systems},
volume={35},
pages={13050--13063},
year={2022}
}
@inproceedings{NEURIPS2023_9f0b1220,
author = {Chau, Siu Lun and Muandet, Krikamol and Sejdinovic, Dino},
booktitle = {Advances in Neural Information Processing Systems},
editor = {A. Oh and T. Naumann and A. Globerson and K. Saenko and M. Hardt and S. Levine},
pages = {50769--50795},
publisher = {Curran Associates, Inc.},
title = {Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models},
url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/9f0b1220028dfa2ee82ca0a0e0fc52d1-Paper-Conference.pdf},
volume = {36},
year = {2023}
}