Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles
M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, Proceedings of the AAAI Conference on Artificial Intelligence 38 (2024) 14388–14396.

cI do not know! but why?d 4 Local model-agnostic example-based explanations of reject
A. Artelt, R. Visser, B. Hammer, Neurocomputing 558 (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, Springer Nature Switzerland, Cham, 2023.

iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios
M. Muschalik, F. Fumagalli, R. Jagtani, B. Hammer, E. Huellermeier, in: Communications in Computer and Information Science, Springer Nature Switzerland, Cham, 2023.

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.

On Feature Removal for Explainability in Dynamic Environments
F. Fumagalli, M. Muschalik, E. Hüllermeier, B. Hammer, in: ESANN 2023 Proceedings, i6doc.com publ., 2023.

SHAP-IQ: Unified Approximation of any-order Shapley Interactions
F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, B. Hammer, in: NeurIPS 2023 - Advances in Neural Information Processing Systems, Curran Associates, Inc., 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.