Artificial Intelligence

Learning via Non-Convex Min-Max Games

Friday, November 15, 2019, 11:00am - 12:00pm PDTiCal
This event is open to the public.
AI Seminar
Meisam Razaviyayn, USC
Recent applications that arise in machine learning have surged significant interest in solving min-max saddle point games. These applications include training generative adversarial networks, defense against adversarial attacks, and fair inference in machine learning, to name just a few.
While the min-max optimization problem has been extensively studied in the convex-concave regime, our knowledge for the non-convex scenario is still very limited. 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 and theory in defense agains adversarial attacks to neural networks, generative adversarial networks, fair learning, and generative adversarial imitation learning.
This is a joint work with Maher Nouiehed (USC), Tianjian Huang (USC), Maziar Sanjabi (EA Sports), Ahmad Beirami (Facebook Research), Jimmy Ba (Vector Institute), and Jason D. Lee (Princeton).
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.
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