Le prochain Mokameeting aura lieu le mercredi 27 octobre 2021 sur Discord à 15h00.
Nous aurons le plaisir d’écouter un exposé de Leonard Wong (University of Toronto).
Titre : Logarithmic divergences: optimal transport, geometry and applications
Résumé : Divergences such as Bregman and KL-divergences are fundamental in probability, statistics and machine learning. In the first part of the talk, we explain how divergences arise naturally from the geometry of optimal transport. Then, we study a family of logarithmic costs which may be regarded as a canonical deformation of the negative dot product in Euclidean quadratic transport. It induces a logarithmic divergence which has remarkable probabilistic and geometric properties. We illustrate its usefulness in statistics and machine learning with several applications.
