[{"year":"2024","intvolume":"       235","page":"14308–14342","citation":{"bibtex":"@inproceedings{Fumagalli_Muschalik_Kolpaczki_Hüllermeier_Hammer_2024, series={Proceedings of Machine Learning Research}, title={KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions}, volume={235}, booktitle={Proceedings of the 41st International Conference on Machine Learning (ICML)}, publisher={PMLR}, author={Fumagalli, Fabian and Muschalik, Maximilian and Kolpaczki, Patrick and Hüllermeier, Eyke and Hammer, Barbara}, year={2024}, pages={14308–14342}, collection={Proceedings of Machine Learning Research} }","short":"F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, B. Hammer, in: Proceedings of the 41st International Conference on Machine Learning (ICML), PMLR, 2024, pp. 14308–14342.","mla":"Fumagalli, Fabian, et al. “KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions.” <i>Proceedings of the 41st International Conference on Machine Learning (ICML)</i>, vol. 235, PMLR, 2024, pp. 14308–14342.","apa":"Fumagalli, F., Muschalik, M., Kolpaczki, P., Hüllermeier, E., &#38; Hammer, B. (2024). KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions. <i>Proceedings of the 41st International Conference on Machine Learning (ICML)</i>, <i>235</i>, 14308–14342.","ama":"Fumagalli F, Muschalik M, Kolpaczki P, Hüllermeier E, Hammer B. KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions. In: <i>Proceedings of the 41st International Conference on Machine Learning (ICML)</i>. Vol 235. Proceedings of Machine Learning Research. PMLR; 2024:14308–14342.","chicago":"Fumagalli, Fabian, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, and Barbara Hammer. “KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions.” In <i>Proceedings of the 41st International Conference on Machine Learning (ICML)</i>, 235:14308–14342. Proceedings of Machine Learning Research. PMLR, 2024.","ieee":"F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, and B. Hammer, “KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions,” in <i>Proceedings of the 41st International Conference on Machine Learning (ICML)</i>, 2024, vol. 235, pp. 14308–14342."},"date_updated":"2025-09-11T16:27:05Z","publisher":"PMLR","volume":235,"author":[{"full_name":"Fumagalli, Fabian","last_name":"Fumagalli","first_name":"Fabian"},{"first_name":"Maximilian","last_name":"Muschalik","full_name":"Muschalik, Maximilian"},{"first_name":"Patrick","full_name":"Kolpaczki, Patrick","last_name":"Kolpaczki"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"},{"first_name":"Barbara","full_name":"Hammer, Barbara","last_name":"Hammer"}],"date_created":"2025-01-16T16:12:16Z","title":"KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions","publication":"Proceedings of the 41st International Conference on Machine Learning (ICML)","type":"conference","abstract":[{"lang":"eng","text":"The Shapley value (SV) is a prevalent approach of allocating credit to machine learning (ML) entities to understand black box ML models. Enriching such interpretations with higher-order interactions is inevitable for complex systems, where the Shapley Interaction Index (SII) is a direct axiomatic extension of the SV. While it is well-known that the SV yields an optimal approximation of any game via a weighted least square (WLS) objective, an extension of this result to SII has been a long-standing open problem, which even led to the proposal of an alternative index. In this work, we characterize higher-order SII as a solution to a WLS problem, which constructs an optimal approximation via SII and k-Shapley values (k-SII). We prove this representation for the SV and pairwise SII and give empirically validated conjectures for higher orders. As a result, we propose KernelSHAP-IQ, a direct extension of KernelSHAP for SII, and demonstrate state-of-the-art performance for feature interactions."}],"status":"public","_id":"58223","project":[{"_id":"109","name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren"},{"_id":"117","name":"TRR 318 - C: TRR 318 - Project Area C"},{"_id":"126","name":"TRR 318 - C3: TRR 318 - Subproject C3"}],"department":[{"_id":"660"}],"series_title":"Proceedings of Machine Learning Research","user_id":"93420","language":[{"iso":"eng"}]}]