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2022-T3 Geometry & Statistics in Data Sciences

Measure-theoretic Approaches and Optimal Transportation in Statistics

Institut Henri Poincaré
Amphithéâtre Hermite
11 rue Pierre et Marie Curie 75005 Paris

Measure-theoretic Approaches and Optimal Transportation in Statistics

21-25 November 2022 - IHP, Paris

The Wasserstein distance in Optimal transportation has proved to be useful for a wide range of learning tasks such as generative models, domain adaptation or supervised embeddings. It is also an important metric for Topological Data Analysis and Geometric inference. More generally, distances on the space of probability measures, such as the maximum mean discrepancy, have shown to be powerful tools in statistical learning.
 

Invited Speakers