Speaker |
Arthur Mensch |
|---|---|
Affiliation |
Ecole Normale Superieure (ENS) Paris |
Date |
Friday, 11 October 2019 |
Time |
13:00-14:00 |
Location |
Zoom |
Link |
1.03 Engineering Building (Malet Place) |
Event series |
Jump Trading/ELLIS CSML Seminar Series |
Abstract |
Building upon recent advances in entropy-regularized optimal transport, and upon Fenchel duality between measures and continuous functions , we propose a generalization of the logistic loss that incorporates a metric or cost between classes. Unlike previous attempts to use optimal transport distances for learning, our loss results in unconstrained convex objective functions, supports infinite (or very large) class spaces, and naturally defines a geometric generalization of the softmax operator. The geometric properties of this loss make it suitable for predicting sparse and singular distributions, for instance supported on curves or hyper-surfaces. We study the theoretical properties of our loss and show-case its effectiveness on two applications: ordinal regression and drawing generation. Arthur Mensch is a post-doctoral researcher at École Normale Supérieure, Paris, in the laboratory of Gabriel Peyré. He holds a Ph.D. in machine learning from the Inria Parietal team. He is currently working in structured prediction, optimal transport and game theory. |
Biography |