Maximilian Muschalik

According to our database, Maximilian Muschalik authored at least 13 papers between 2022 and 2024.

Collaborative distances:

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

2024

Stakeholder-centric explanations for black-box decisions: an XAI process model and its application to automotive goodwill assessments.

Stefan Haas, Konstantin Hegestweiler, Michael Rapp, Maximilian Muschalik, Eyke Hüllermeier

Frontiers Artif. Intell., 2024

shapiq: Shapley Interactions for Machine Learning.

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

CoRR, 2024

Explaining Change in Models and Data with Global Feature Importance and Effects.

Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier

Proceedings of the Workshop on Explainable AI for Time Series and Data Streams (TempXAI 2024) co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2024), 2024

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

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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 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

On Feature Removal for Explainability in Dynamic Environments.

Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer

Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

2022

Agnostic Explanation of Model Change based on Feature Importance.

Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier

Künstliche Intell., 2022