Maximilian Muschalik

According to our database, Maximilian Muschalik authored at least 8 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification.

Patrick Kolpaczki, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier

CoRR, 2024

Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles.

Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Approximating the Shapley Value without Marginal Contributions.

Patrick Kolpaczki, Viktor Bengs, Maximilian Muschalik, Eyke Hüllermeier

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Incremental permutation feature importance (iPFI): towards online explanations on data streams.

Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer

Mach. Learn., December, 2023

iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios.

Maximilian Muschalik, Fabian Fumagalli, Rohit Jagtani, Barbara Hammer, Eyke Hüllermeier

Proceedings of the Explainable Artificial Intelligence, 2023

iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams.

Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

SHAP-IQ: Unified Approximation of any-order Shapley Interactions.

Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, Barbara Hammer

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Agnostic Explanation of Model Change based on Feature Importance.

Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier

Künstliche Intell., 2022