Siqi Zhang, “The Complexity of Nonconvex Minimax Optimization: Fundamental Limits and Generalization”
Siqi Zhang, PhD
Postdoctoral Fellow
Johns Hopkins University
“The Complexity of Nonconvex Minimax Optimization: Fundamental Limits and
Generalization”
Abstract: We will study the complexity for finding approximate stationary points in Nonconvex-Strongly-Concave (NC-SC) smooth minimax optimization problems. We establish nontrivial lower complexity bounds for NC-SC problems in both general and stochastic settings, which reveal substantial gaps between these limits and best-known upper bounds in the literature, which later we close by proposing a new algorithm based on a generic Catalyst acceleration scheme. In the second part of the talk, we will further extend our analysis to the purely stochastic case and introduce some of our recent results on the generalization performances in nonconvex minimax optimization.
Biography: Siqi Zhang is a Rufus Isaacs Postdoctoral Fellow in the Department of Applied Mathematics and Statistics (AMS) of Johns Hopkins University (JHU). Prior to moving to JHU, he obtained his PhD from University of Illinois at Urbana-Champaign (UIUC) in 2022. His research mainly focuses on optimization theory and algorithms, also their intersection with machine learning.
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Meeting ID: 986 2441 3365