3 papers
cs.LG2026
Reliable Modeling of Distribution Shifts via Displacement-Reshaped Optimal Transport
Philip Naumann, Jacob Kauffmann, Klaus-Robert Müller +1
Optimal transport (OT) is a central framework for modeling distribution shifts. Because OT compares distributions directly in input space, a well-designed ground metric between obs…
cs.LG2026
Wasserstein Distances Made Explainable: Insights Into Dataset Shifts and Transport Phenomena
Philip Naumann, Jacob Kauffmann, Grégoire Montavon
Wasserstein distances provide a powerful framework for comparing data distributions. They can be used to analyze processes over time or to detect inhomogeneities within data. Howev…
eess.IV2025
Towards Robust Foundation Models for Digital Pathology
Jonah Kömen, Edwin D. de Jong, Julius Hense +9
Biomedical Foundation Models (FMs) are rapidly transforming AI-enabled healthcare research and entering clinical validation. However, their susceptibility to learning non-biologica…