📚 Bibliography¶
This page lists all references used in the documentation, including algorithms and related software tools.
Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, and Himabindu Lakkaraju. Openxai: towards a transparent evaluation of model explanations. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022 (NeurIPS 2022). 2022. URL: http://papers.nips.cc/paper\_files/paper/2022/hash/65398a0eba88c9b4a1c38ae405b125ef-Abstract-Datasets\_and\_Benchmarks.html.
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, and Pieter-Jan Kindermans. Innvestigate neural networks! J. Mach. Learn. Res., 20:93:1–93:8, 2019. URL: https://jmlr.org/papers/v20/18-540.html.
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, and Yunfeng Zhang. AI explainability 360: an extensible toolkit for understanding data and machine learning models. J. Mach. Learn. Res., 21:130:1–130:6, 2020. URL: https://jmlr.org/papers/v21/19-1035.html.
Hubert Baniecki, Wojciech Kretowicz, Piotr Piatyszek, Jakub Wisniewski, and Przemyslaw Biecek. Dalex: responsible machine learning with interactive explainability and fairness in python. J. Mach. Learn. Res., 22:214:1–214:7, 2021. URL: https://jmlr.org/papers/v22/20-1473.html.
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, and Joaquin Vanschoren. Openml benchmarking suites. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS 2021). 2021. URL: https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/c7e1249ffc03eb9ded908c236bd1996d-Abstract-round2.html.
Liam Butler, Jae Shin Kang, Aryan Agarwal, Yusef Erginbas, Bin Yu, and Kannan Ramchandran. ProxySPEX: inference-efficient interpretability via sparse feature interactions in LLMs. CoRR, 2025. arXiv:2505.17495.
Javier Castro, Daniel Gómez, and Juan Tejada. Polynomial calculation of the Shapley value based on sampling. Computers & Operations Research, 36(5):1726–1730, 2009. doi:10.1016/j.cor.2008.04.004.
Ian Covert and Su-In Lee. Improving kernelshap: practical shapley value estimation using linear regression. In The 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021), volume 130 of Proceedings of Machine Learning Research, 3457–3465. PMLR, 2021. URL: http://proceedings.mlr.press/v130/covert21a.html.
Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke HĂĽllermeier, and Barbara Hammer. SHAP-IQ: unified approximation of any-order shapley interactions. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, (NeurIPS 2023). 2023. URL: http://papers.nips.cc/paper\_files/paper/2023/hash/264f2e10479c9370972847e96107db7f-Abstract-Conference.html.
Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke HĂĽllermeier, and Barbara Hammer. Kernelshap-iq: weighted least square optimization for shapley interactions. In Forty-first International Conference on Machine Learning (ICML 2024). OpenReview.net, 2024. URL: https://openreview.net/forum?id=d5jXW2H4gg.
Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, and Marina M.-C. Höhne. Quantus: an explainable AI toolkit for responsible evaluation of neural network explanations and beyond. J. Mach. Learn. Res., 24:34:1–34:11, 2023. URL: https://jmlr.org/papers/v24/22-0142.html.
Kevin Fu Jiang, Weixin Liang, James Y. Zou, and Yongchan Kwon. Opendataval: a unified benchmark for data valuation. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023 (NeurIPS 2023). 2023. URL: http://papers.nips.cc/paper\_files/paper/2023/hash/5b047c7d862059a5df623c1ce2982fca-Abstract-Datasets\_and\_Benchmarks.html.
Jae Shin Kang, Liam Butler, Aryan Agarwal, Yusef Erginbas, Ramtin Pedarsani, Kannan Ramchandran, and Bin Yu. SPEX: scaling feature interaction explanations for LLMs. CoRR, 2025. arXiv:2502.13870.
Janis Klaise, Arnaud Van Looveren, Giovanni Vacanti, and Alexandru Coca. Alibi explain: algorithms for explaining machine learning models. J. Mach. Learn. Res., 22:181:1–181:7, 2021. URL: https://jmlr.org/papers/v22/21-0017.html.
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson. Captum: A unified and generic model interpretability library for pytorch. CoRR, 2020. URL: https://arxiv.org/abs/2009.07896, arXiv:2009.07896.
Patrick Kolpaczki, Viktor Bengs, Maximilian Muschalik, and Eyke Hüllermeier. Approximating the shapley value without marginal contributions. In Thirty-Eighth AAAI Conference on Artificial Intelligence, (AAAI 2024), 13246–13255. AAAI Press, 2024. doi:10.1609/AAAI.V38I12.29225.
Patrick Kolpaczki, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, and Eyke Hüllermeier. SVARM-IQ: efficient approximation of any-order shapley interactions through stratification. In International Conference on Artificial Intelligence and Statistics (AISTATS 2024), volume 238 of Proceedings of Machine Learning Research, 3520–3528. PMLR, 2024. URL: https://proceedings.mlr.press/v238/kolpaczki24a.html.
Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai, Himabindu Lakkaraju, and Haoyi Xiong. M\(^\mbox 4\): A unified XAI benchmark for faithfulness evaluation of feature attribution methods across metrics, modalities and models. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023 (NeurIPS 2023). 2023. URL: http://papers.nips.cc/paper\_files/paper/2023/hash/05957c194f4c77ac9d91e1374d2def6b-Abstract-Datasets\_and\_Benchmarks.html.
Yang Liu, Sujay Khandagale, Colin White, and Willie Neiswanger. Synthetic benchmarks for scientific research in explainable machine learning. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS 2021). 2021. URL: https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/c16a5320fa475530d9583c34fd356ef5-Abstract-round2.html.
Scott M. Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, 4765–4774. 2017. URL: https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html.
Sasan Maleki, Long Tran-Thanh, Greg Hines, Talal Rahwan, and Alex Rogers. Bounding the estimation error of sampling-based shapley value approximation with/without stratifying. CoRR, 2013. arXiv:1306.4265.
Maximilian Muschalik, Hubert Baniecki, Fabian Fumagalli, Patrick Kolpaczki, Barbara Hammer, and Eyke HĂĽllermeier. Shapiq: shapley interactions for machine learning. In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, (NeurIPS 2024). 2024. URL: http://papers.nips.cc/paper\_files/paper/2024/hash/eb3a9313405e2d4175a5a3cfcd49999b-Abstract-Datasets\_and\_Benchmarks\_Track.html.
Ramin Okhrati and Aldo Lipani. A multilinear sampling algorithm to estimate shapley values. CoRR, 2020. arXiv:2010.12082, doi:10.48550/arXiv.2010.12082.
Lars Henry Berge Olsen, Ingrid Kristine Glad, Martin Jullum, and Kjersti Aas. A comparative study of methods for estimating model-agnostic shapley value explanations. Data Min. Knowl. Discov., 38(4):1782–1829, 2024. doi:10.1007/S10618-024-01016-Z.
Guilherme Dean Pelegrina, Leonardo Tomazeli Duarte, and Michel Grabisch. A \emph k-additive choquet integral-based approach to approximate the SHAP values for local interpretability in machine learning. Artif. Intell., 325:104014, 2023. doi:10.1016/J.ARTINT.2023.104014.
Che-Ping Tsai, Chih-Kuan Yeh, and Pradeep Ravikumar. Faith-shap: the faithful shapley interaction index. J. Mach. Learn. Res., 24:94:1–94:42, 2023. URL: https://jmlr.org/papers/v24/22-0202.html.
R. Teal Witter, Yurong Liu, and Christopher Musco. Regression-adjusted monte carlo estimators for shapley values and probabilistic values. In Proceedings of Advances in Neural Information Processing Systems (NeurIPS). 2025. URL: https://openreview.net/forum?id=Qabko39AS5.