17 citations · 21 across the 4 of their papers we have counts for
5 papers
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…
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…
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…
Consequence-aware Sequential Counterfactual Generation
Philip Naumann, Eirini Ntoutsi
Counterfactuals have become a popular technique nowadays for interacting with black-box machine learning models and understanding how to change a particular instance to obtain a de…
Helping Computers Understand Geographically-Bound Activity Restrictions
M. Soll, P. Naumann, J. Schöning +2
The lack of certain types of geographic data prevents the development of location-aware technologies in a number of important domains. One such type of "unmapped" geographic data i…