Learning via Non-Convex Min-Max Games

Thu, Oct 17, 2019, 4:30 pm to 5:30 pm
Speaker(s): 
Sponsor(s): 
Electrical Engineering
Center for Statistics and Machine Learning

Abstract:

Recent applications that arise in machine learning have surged significant interest in solving min-max saddle point games. This problem has been extensively studied in the convex-concave regime for which a global equilibrium solution can be computed efficiently. In this talk, we study the problem in the non-convex regime and show that an $\epsilon$--first order stationary point of the game can be computed  when one of the player’s objective can be optimized to global optimality efficiently.  We discuss the application of the proposed algorithm in defense agains adversarial attacks to neural networks, generative adversarial networks, fair learning, and generative adversarial imitation learning. 

Bio:

Meisam Razaviyayn is an assistant professor of Industrial and Systems Engineering and Computer Science at the University of Southern California. Prior to joining USC, he was a postdoctoral research fellow in the Department of Electrical Engineering at Stanford University. He received his PhD in Electrical Engineering with minor in Computer Science at the University of Minnesota under the supervision of Professor Tom Luo. He obtained his MS degree in Mathematics under the supervision of Professor Gennady Lyubeznik. Meisam Razaviyayn is the recipient of IEEE Data Science Workshop Best Paper Award in 2019, the Signal Processing Society Young Author Best Paper Award in 2014, and the finalist for Best Paper Prize for Young Researcher in Continuous Optimization in 2013 and 2016. His research interests include the design and analysis of large scale optimization algorithms arise in modern data science era.