S04E02-1: The one with Xiugang Wu talking about Information Constrained Optimal Transport (Part 1)

Title: Information Constrained Optimal Transport: From Talagrand, to Marton, to Cover

Abstract: The optimal transport problem studies how to transport one measure to another in the most cost-effective way and has a wide range of applications from economics to machine learning. In this talk, we introduce and study an information constrained variation of this problem. Our study yields a strengthening and generalization of Talagrand's celebrated transportation cost inequality. Following Marton's approach, we show that the new transportation cost inequality can be used to recover old and new concentration of measure results. Finally, we provide an application of this new inequality to network information theory. We show that it can be used to recover almost immediately a recent solution to a long-standing open problem posed by Cover regarding the capacity of the relay channel.

Bio: Xiugang Wu is an assistant professor at the University of Delaware, where he is jointly appointed in the Department of Electrical and Computer Engineering and the Department of Computer and Information Sciences, and also affiliated with the Data Science Institute. Previously, he was a postdoctoral fellow in the Department of Electrical Engineering at Stanford University, and received his Ph.D. degree in Electrical and Computer Engineering from the University of Waterloo. His research interests are in information theory, networks, data science, and the interplay between them. He is a recipient of the 2017 NSF Center for Science of Information (CSoI) Postdoctoral Fellowship.

#OptimalTransport#NetworkInformationTheory#RelayChannel#Xiugang_Wu#Salim_ElRouayheb#InformationTheory#ShannonChannel