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Regularity Properties of Entropical Optimal Transport


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We are pleased to announce the next MaLGa Seminar Series - Statistical Learning and optimization.

Speaker: Giulia Luise

Affiliation: Imperial College

Date: Tuesday, March 16th, 2021

Time: 15:00 p.m.


Online streaming on Zoom - see first useful link

Online streaming on YouTube - see second useful link


Regularity properties of Entropic Optimal Transport in applications to machine learning


The entropic regularization has proved to be a powerful tool to define approximations of optimal transport distances with improved  computational and statistical aspects.

In this talk we will focus on further advantages of such entropic regularization, in terms of smoothness. We discuss its regularity properties and their role in some machine learning problems where  regularized optimal transport is used as discrepancy metric in supervised and unsupervised frameworks.

Bio: Giulia Luise has recently obtained her PhD in Machine Learning  at UCL, London, under the supervision of Massimiliano Pontil and Carlo Ciliberto. Her main research interest focuses on the interplay of optimal transport and machine learning.

She is now a Research Associate at Imperial College, where she started working on reinforcement learning.