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Statistics Seminar

September 23 @ 10:00 am - 11:00 am

Title: Learning with entropy-regularized optimal transport 

Presenter: Dr. Aude Genevay

Abstract: Entropy-regularized OT (EOT) was first introduced by Cuturi in 2013 as a solution to the computational burden of OT for machine learning problems. In this talk, after studying the properties of EOT, we will introduce a new family of losses between probability measures called Sinkhorn Divergences. Based on EOT, this family of losses actually interpolates between OT (no regularization) and MMD (infinite regularization). We will illustrate these theoretical claims on a set of learning problems formulated as minimizations over the space of measures

Link: https://uwmadison.webex.com/meet/pr923156234

Details

Date:
September 23
Time:
10:00 am - 11:00 am