Philip Naumann
I am a PhD student in Machine Learning at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at Technische Universität Berlin, supervised by Prof. Klaus-Robert Müller and Prof. Grégoire Montavon. My research centers on explaining and modeling distribution shifts through the lens of optimal transport and XAI. I am interested in both foundational questions and applications in settings where model reliability and robustness matter, such as digital pathology and industrial processes.News
- Jun 2026Paper Towards Robust Foundation Models for Digital Pathology published in Nature Communications.
- May 2026New preprint: Reliable Modeling of Distribution Shifts via Displacement-Reshaped Optimal Transport on arXiv.
- Apr 2026Co-organized the Special Session on Efficient and Resilient Machine Learning for Industrial Applications at ESANN 2026.
- Jan 2026Paper Wasserstein Distances Made Explainable published in IEEE Transactions on Pattern Analysis and Machine Intelligence.
Publications
Selected Publications
Nature Communications, 17(1):5218, 2026
@article{komen2026pathology,
title = {Towards Robust Foundation Models for Digital Pathology},
author = {Jonah K{\"o}men and Edwin D. de Jong and Julius Hense and Hannah Marienwald and Jonas Dippel and Philip Naumann and Eric Marcus and Lukas Ruff and Maximilian Alber and Jonas Teuwen and Frederick Klauschen and Klaus-Robert M{\"u}ller},
journal = {Nature Communications},
year = {2026},
volume = {17},
number = {1},
pages = {5218},
doi = {10.1038/s41467-026-73923-2}
}
arXiv preprint, 2026
@misc{naumann2026reshapeOT,
title = {Reliable Modeling of Distribution Shifts via Displacement-Reshaped Optimal Transport},
author = {Philip Naumann and Jacob Kauffmann and Klaus-Robert M{\"u}ller and Gr{\'e}goire Montavon},
year = {2026},
eprint = {2605.04965},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2605.04965}
}
IEEE Transactions on Pattern Analysis and Machine Intelligence, 48(6):6393-6406, 2026
@article{naumann2026wax,
title = {Wasserstein Distances Made Explainable: Insights Into Dataset Shifts and Transport Phenomena},
author = {Philip Naumann and Jacob Kauffmann and Gr{\'e}goire Montavon},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2026},
volume = {48},
number = {6},
pages = {6393--6406},
doi = {10.1109/TPAMI.2026.3656947}
}
Other Publications
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2026
@inproceedings{wissmann2026esann,
title = {Efficient and Resilient Machine Learning for Industrial Applications},
author = {Philipp Wissmann and Philip Naumann and Daniel Hein and Steffen Udluft and Marc Weber and Simon Leszek and Thomas Runkler},
booktitle = {34th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
year = {2026},
doi = {10.14428/esann/2026.ES2026-6}
}
xAI (Late-breaking Work, Demos, Doctoral Consortium), CEUR Workshop Proceedings, vol. 3793, pp. 425-432, 2024
@inproceedings{naumann2024xai,
title = {Towards {XAI} for Optimal Transport},
author = {Philip Naumann},
booktitle = {xAI (Late-breaking Work, Demos, Doctoral Consortium)},
series = {CEUR Workshop Proceedings},
volume = {3793},
pages = {425--432},
publisher = {CEUR-WS.org},
year = {2024}
}
ECML/PKDD, Lecture Notes in Computer Science, vol. 12976, pp. 682-698, 2021
@inproceedings{naumann2021ecml,
title = {Consequence-Aware Sequential Counterfactual Generation},
author = {Philip Naumann and Eirini Ntoutsi},
booktitle = {{ECML/PKDD}},
series = {Lecture Notes in Computer Science},
volume = {12976},
pages = {682--698},
publisher = {Springer},
year = {2021},
doi = {10.1007/978-3-030-86520-7_42}
}