Can You Tell the Difference? Contrastive Explanations for ABox Entailments
P. Koopmann, Y. Mahmood, A.-C. Ngonga Ngomo, B. Tiwari, in: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), 2026, pp. 19189–19197.

Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks
M. Muschalik, F. Fumagalli, P. Frazzetto, J. Strotherm, L. Hermes, A. Sperduti, E. Hüllermeier, B. Hammer, in: The Thirteenth International Conference on Learning Representations (ICLR), 2025.

Explaining Outliers using Isolation Forest and Shapley Interactions
R. Visser, F. Fumagalli, E. Hüllermeier, B. Hammer, in: Proceedings of the European Symposium on Artificial Neural Networks (ESANN), 2025.

Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory
F. Fumagalli, M. Muschalik, E. Hüllermeier, B. Hammer, J. Herbinger, in: Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 2025, pp. 5140–5148.

No learning rates needed: Introducing SALSA - Stable Armijo Line Search Adaptation
P. Kenneweg, T. Kenneweg, F. Fumagalli, B. Hammer, in: 2024 International Joint Conference on Neural Networks (IJCNN), 2024, pp. 1–8.

Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles
M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, in: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024, pp. 14388–14396.

SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification
P. Kolpaczki, M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, in: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 2024, pp. 3520–3528.

KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions
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.

shapiq: Shapley interactions for machine learning
M. Muschalik, H. Baniecki, F. Fumagalli, P. Kolpaczki, B. Hammer, E. Huellermeier, in: Advances in Neural Information Processing Systems (NeurIPS), 2024, pp. 130324–130357.

Approximating the shapley value without marginal contributions
P. Kolpaczki, V. Bengs, M. Muschalik, E. Hüllermeier, in: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024, pp. 13246–13255.

SNAPE: A Sequential Non-Stationary Decision Process Model for Adaptive Explanation Generation
A. Robrecht, S. Kopp, in: Proceedings of the 15th International Conference on Agents and Artificial Intelligence, SCITEPRESS - Science and Technology Publications, 2023, pp. 48–58.

Incremental permutation feature importance (iPFI): towards online explanations on data streams
F. Fumagalli, M. Muschalik, E. Hüllermeier, B. Hammer, Machine Learning 112 (2023) 4863–4903.

iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios
M. Muschalik, F. Fumagalli, R. Jagtani, B. Hammer, E. Huellermeier, in: Proceedings of the World Conference on Explainable Artificial Intelligence (XAI), 2023.

iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams
M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, in: Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD), Springer Nature Switzerland, 2023.

On Feature Removal for Explainability in Dynamic Environments
F. Fumagalli, M. Muschalik, E. Hüllermeier, B. Hammer, in: Proceedings of the European Symposium on Artificial Neural Networks (ESANN), 2023.

SHAP-IQ: Unified Approximation of any-order Shapley Interactions
F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, B. Hammer, in: Advances in Neural Information Processing Systems (NeurIPS), 2023, pp. 11515--11551.

Agnostic Explanation of Model Change based on Feature Importance
M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, KI - Künstliche Intelligenz 36 (2022) 211–224.