4 citations · 4 across the 2 of their papers we have counts for
3 papers · 1 filter
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…
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…