IEEE transactions on pattern analysis and machine intelligence
Wasserstein Distances Made Explainable: Insights into Dataset Shifts and Transport Phenomena.
Philip Naumann, Jacob Kauffmann, Gregoire Montavon
Published: 202610.1109/TPAMI.2026.3656947
Abstract
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. However, simply calculating the Wasserstein distance or analyzing the corre…
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