In this talk, we will develop a principled framework for neural estimation and optimization of information measures and specifically directed information, which is then leveraged to estimate the feedforward and feedback capacities of general communication channels. To that end, we propose a novel Directed Information Neural Estimator (DINE) that complements the Mutual Information Neural Estimation (MINE), and then develop methods for optimizing DINE and MINE over the channel input distributions. More specifically, two optimization methods are proposed, one for continuous channel input spaces and the other for discrete. While capacity estimation is the main application considered in this talk, we will discuss how the developed estimation and optimization techniques are applicable in additional scenarios where (maximized) directed information is of interest, such as probability density estimation for processes with memory, causality identification, and machine learning in general.
The talk is based on a joint work with Dor Tzur, Ziv Aharoni and Ziv Goldfeld.
Bio: Haim Permuter received his B.Sc. (summa cum laude) from Ben-Gurion University (BGU) and Ph.D. from Stanford University, both in Electrical Engineering, in 1997 and 2008, respectively. Between 1997-2004, he served as a scientific research officer in an R&D unit in the Israeli Defense Forces. In summer 2002 he worked for IBM, Almaden research center. He is a recipient of several rewards including Eshkol Fellowship, Wolf Award, Fulbright Fellowship, Stanford Graduate Fellowship, U.S.-Israel Binational Science Foundation Bergmann Memorial Award, and Allon Fellowship. Haim joined the faculty of Electrical Engineering Department at BGU in Oct 2008 as a tenure-track faculty, and is now a Professor, Luck-Hille Chair in Electrical Engineering. Haim serves as head of the communication, cyber and information track in his department. Haim served on the editorial boards of the IEEE Transactions on Information Theory between 2013-2016.