Explainable AI: A Unified Approach Based on Cooperative Game Theory
Fumagalli F (2025)
Bielefeld: Universität Bielefeld.
postdoc
ffumagalli@techfak.uni-bielefeld.deRoom: CITEC 2-112
Explainable AI: A Unified Approach Based on Cooperative Game Theory
Fumagalli F (2025)
Bielefeld: Universität Bielefeld.
KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions
Fumagalli F, Muschalik M, Kolpaczki P, Hüllermeier E, Hammer B (2024)
In: Proceedings of the 41st International Conference on Machine Learning. Salakhutdinov R, Kolter Z, Heller K, Weller A, Oliver N, Scarlett J, Berkenkamp F (Eds); Proceedings of Machine Learning Research, 235. PMLR: 14308-14342.
SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification
Kolpaczki P, Muschalik M, Fumagalli F, Hammer B, Hüllermeier E (2024)
In: International Conference on Artificial Intelligence and Statistics, 2-4 May 2024, Palau de Congressos, Valencia, Spain. Dasgupta S, Mandt S, Li Y (Eds); Proceedings of Machine Learning Research, 238. San Diego: PMLR: 3520-3528.
Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles
Muschalik M, Fumagalli F, Hammer B, Hüllermeier E (2024)
Proceedings of the AAAI Conference on Artificial Intelligence 38(13): 14388-14396.
iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios
Muschalik M, Fumagalli F, Jagtani R, Hammer B, Hüllermeier E (2023)
In: Explainable Artificial Intelligence. First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part I. Longo L (Ed); Communications in Computer and Information Science. Cham: Springer Nature Switzerland: 177-194.
SHAP-IQ: Unified Approximation of any-order Shapley Interactions
Fumagalli F, Muschalik M, Kolpaczki P, Hüllermeier E, Hammer B (2023)
In: Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Advances in Neural Information Processing Systems. .
On Feature Removal for Explainability in Dynamic Environments
Fumagalli F, Muschalik M, Hüllermeier E, Hammer B (2023)
In: ESANN 2023 proceedings. 83-88.
Incremental permutation feature importance (iPFI): towards online explanations on data streams
Fumagalli F, Muschalik M, Hüllermeier E, Hammer B (2023)
Machine Learning .
iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams
Muschalik M, Fumagalli F, Hammer B, Hüllermeier E (2023)
In: Machine Learning and Knowledge Discovery in Databases: Research Track. European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part III. Koutra D, Plant C, Gomez Rodriguez M, Baralis E, Bonchi F (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 428-445.
Agnostic Explanation of Model Change based on Feature Importance
Muschalik M, Fumagalli F, Hammer B, Hüllermeier E (2022)
KI - Künstliche Intelligenz.