[1] Kjersti Aas, Martin Jullum, and Anders Løland. Explaining individual predictions when features are dependent: More accurate approximations to Shapley values. Artif. Intell., 298:103502, 2021.
[2] Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. Sanity checks for saliency maps. In Advances in Neural Information Processing Systems, volume 31, 2018.
[3] Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, and José Miguel Hernández-Lobato. Getting a CLUE: A method for explaining uncertainty estimates. In International Conference on Learning Representations, 2021.
[4] Stephen Bates, Emmanuel Candès, Lihua Lei, Yaniv Romano, and Matteo Sesia. Testing for outliers with conformal p-values. The Annals of Statistics, 51(1):149 - 178, 2023.
[5] Blair Bilodeau, Natasha Jaques, Pang Wei Koh, and Been Kim. Impossibility theorems for feature attribution. arXiv preprint, 2212.11870, 2022.
[6] Sebastian Bordt and Ulrike von Luxburg. From Shapley values to generalized additive models and back. In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, volume 206, pages 709-745, 2023.
[7] Craig Boutilier, Nir Friedman, Moises Goldszmidt, and Daphne Koller. Context-specific independence in Bayesian networks. In Proceedings of The 12th Conference on Uncertainty in Artificial Intelligence, 1996.
[8] Hugh Chen, Joseph D. Janizek, Scott Lundberg, and Su-In Lee. True to the model or true to the data? arXiv preprint, 2006.16234, 2020.
[9] Hugh Chen, Ian C. Covert, Scott M. Lundberg, and Su-In Lee. Algorithms to estimate Shapley value feature attributions. arXiv preprint, 2207.07605, 2022.
[10] Jianbo Chen, Le Song, Martin Wainwright, and Michael Jordan. Learning to explain: An information-theoretic perspective on model interpretation. In Proceedings of the 35th International Conference on Machine Learning, pages 883-892, 2018.
[11] Jianbo Chen, Le Song, Martin J. Wainwright, and Michael I. Jordan. L-Shapley and C-Shapley: Efficient model interpretation for structured data. In International Conference on Learning Representations, 2019.
[12] Tianqi Chen and Carlos Guestrin. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, page 785-794, 2016.
[13] Thomas M. Cover and Joy A. Thomas. Elements of information theory. John Wiley & Sons, Hoboken, NJ, second edition, 2006.
[14] Ian Covert and Su-In Lee. Improving kernelSHAP: Practical Shapley value estimation using linear regression. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, volume 130, pages 3457-3465, 2021.
[15] Ian Covert, Scott M. Lundberg, and Su-In Lee. Understanding global feature contributions with additive importance measures. In Advances in Neural Information Processing Systems, volume 33, pages 17212-17223, 2020.
[16] Ian Covert, Scott M. Lundberg, and Su-In Lee. Explaining by removing: A unified framework for model explanation. J. Mach. Learn. Res., 22(209):1-90, 2021.
[17] Dheeru Dua and Casey Graff. UCI machine learning repository, 2017. URL http://archive.ics.uci.edu/ml.
[18] Yoav Freund, H Sebastian Seung, Eli Shamir, and Naftali Tishby. Selective sampling using the query by committee algorithm. Machine learning, 28(2):133-168, 1997.
[19] Jerome H. Friedman. Multivariate adaptive regression splines. The Annals of Statistics, 19(1):1-67, 1991.