Maximilian Muschalik

Akademiestr.7

80799 Munich

Hi, I am Maximilian Muschalik, and I am a PhD student at Prof. Eyke Hüllermeier’s AIML chair, LMU Munich. I also like Shapley interactions quite a lot. So I work on them and develop shapiq.

Research Focus

My research centers on the topic of explainable artificial intelligence (XAI), with a focus on black-box machine learning models.

Currently, I am mainly working on the topic of Shapley-based explanations and extensions of Shapley values to higher-order interactions. Therein, we are mostly concerned with developing approximation methods to compute Shapley interactions efficiently, as usually the computational complexity of Shapley interactions is infeasible for most real-world applications. We have bundled our research and methods into the Python package shapiq, which unifies state-of-the-art algorithms to efficiently compute Shapley values and any-order Shapley interactions in an application-agnostic framework. More recently, we have pushed Shapley-based explanations into new modalities, including graph neural networks, vision-language encoders, and hyperparameter optimization. If you are interested in Shapley interactions, I would be happy to hear from you. You can also check out our blog post on Shapley interactions.

Another interesting area of XAI research is the development of methods that can explain the predictions of models in dynamic learning environments. Specifically, we investigate the challenges of creating accurate and timely explanations for models that must constantly adapt to changes in data streams and learning tasks. In such dynamic settings, traditional XAI methods may be computationally expensive or unable to provide faithful explanations in a timely manner.

My research is part of the TRR 318 Constructing Explainability.

news

Jan 20, 2026	Our paper HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization has been presented at AAAI 2026 in Singapore.
Dec 5, 2025	Our paper Explaining Similarity in Vision-Language Encoders with Weighted Banzhaf Interactions has been presented at NeurIPS 2025 in San Diego.
Nov 8, 2025	Our paper Investigating the Impact of Conceptual Metaphors on LLM-based NLI through Shapley Interactions has been presented at EMNLP 2025 Findings in Suzhou.
May 5, 2025	Our paper Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory has been presented at AISTATS 2025.
May 3, 2025	Our paper Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks has been presented at ICLR 2025.

Latest Posts

Dec 5, 2024	What Are Shapley Interactions, and Why Should You Care?

Selected Publications

2025

ICLR

Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks

Maximilian Muschalik, Fabian Fumagalli, Paolo Frazzetto, Janine Strotherm, Luca Hermes, Alessandro Sperduti, Eyke Hüllermeier, and Barbara Hammer

In The Thirteenth International Conference on Learning Representations (ICLR 2025), 2025

Bib Poster Website

@inproceedings{Muschalik.2025,
  title = {Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks},
  author = {Muschalik, Maximilian and Fumagalli, Fabian and Frazzetto, Paolo and Strotherm, Janine and Hermes, Luca and Sperduti, Alessandro and H{\"u}llermeier, Eyke and Hammer, Barbara},
  year = {2025},
  booktitle = {The Thirteenth International Conference on Learning Representations (ICLR 2025)},
  url = {https://openreview.net/forum?id=9tKC0YM8sX},
}

NeurIPS

Explaining Similarity in Vision-Language Encoders with Weighted Banzhaf Interactions

Hubert Baniecki, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier, and Przemyslaw Biecek

In Advances in Neural Information Processing Systems 38 (NeurIPS 2025), 2025

Bib Website

@inproceedings{Baniecki.2025,
  author = {Baniecki, Hubert and Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and H{\"u}llermeier, Eyke and Biecek, Przemyslaw},
  year = {2025},
  booktitle = {Advances in Neural Information Processing Systems 38 (NeurIPS 2025)},
  url = {https://openreview.net/forum?id=on22Rx5A4F},
}

2024

ICML

KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions

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

In Forty-first International Conference on Machine Learning (ICML 2024), 2024

Bib Poster

@inproceedings{Fumagalli.2024,
  title = {Kernel{SHAP}-{IQ}: Weighted Least Square Optimization for Shapley Interactions},
  author = {Fumagalli, Fabian and Muschalik, Maximilian and Kolpaczki, Patrick and H{\"u}llermeier, Eyke and Hammer, Barbara},
  year = {2024},
  booktitle = {Forty-first International Conference on Machine Learning (ICML 2024)},
  url = {https://openreview.net/forum?id=d5jXW2H4gg},
}

AAAI

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

Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, and Eyke Hüllermeier

In Thirty-Eighth AAAI Conference on Artificial Intelligence, (AAAI 2024), 2024

Bib Poster

@inproceedings{Muschalik.2024,
  title = {Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles},
  author = {Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and H{\"{u}}llermeier, Eyke},
  year = {2024},
  booktitle = {Thirty-Eighth {AAAI} Conference on Artificial Intelligence, (AAAI 2024)},
  publisher = {{AAAI} Press},
  pages = {14388--14396},
  doi = {10.1609/AAAI.V38I12.29225},
  url = {https://doi.org/10.1609/aaai.v38i12.29225},
}

NeurIPS

shapiq: Shapley Interactions for Machine Learning

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

In The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track (NeurIPS 2024), 2024

Bib Poster Website

@inproceedings{Muschalik.2024a,
  title = {shapiq: Shapley Interactions for Machine Learning},
  author = {Muschalik, Maximilian and Baniecki, Hubert and Fumagalli, Fabian and Kolpaczki, Patrick and Hammer, Barbara and H{\"u}llermeier, Eyke},
  year = {2024},
  booktitle = {The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track (NeurIPS 2024)},
  url = {https://openreview.net/forum?id=knxGmi6SJi},
}

2023

NeurIPS

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

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

In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 2023

Bib Poster

@inproceedings{Fumagalli.2023b,
  title = {{SHAP-IQ:} Unified Approximation of any-order Shapley Interactions},
  author = {Fumagalli, Fabian and Muschalik, Maximilian and Kolpaczki, Patrick and H{\"{u}}llermeier, Eyke and Hammer, Barbara},
  year = {2023},
  booktitle = {Advances in Neural Information Processing Systems 36 (NeurIPS 2023)},
  url = {https://openreview.net/forum?id=IEMLNF4gK4},
}

ECMLPKDD

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

Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, and Eyke Hüllermeier

In Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, (ECML PKDD 2023), 2023

Bib Poster Slides

@inproceedings{Muschalik.2023a,
  title = {iSAGE: An Incremental Version of {SAGE} for Online Explanation on Data Streams},
  author = {Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and H{\"{u}}llermeier, Eyke},
  year = {2023},
  booktitle = {Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, ({ECML} {PKDD} 2023)},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = {14171},
  pages = {428--445},
  doi = {10.1007/978-3-031-43418-1\_26},
  url = {https://doi.org/10.1007/978-3-031-43418-1\_26},
}