Regularity properties of Entropic Optimal Transport in applications to machine learning
Giulia Luise - Imperial College
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.
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.
2021-03-16 at 3:00 pm