Generalization Analysis of Biased Stochastic Gradient Methods for Minimax Problems

arXiv cs.AIOctober 15, 2026en

arXiv cs.AI

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arXiv:2610.19322v1 Announce Type: new Abstract: Minimax optimization refers to a class of optimization problems with some variables to minimize and other variables to maximize. Biased stochastic gradient methods have shown practical success in solving minimax problems to either improve robustness, enhance communication efficiency, or decrease computational costs. These successes motivate a lot of theoretical works to study the convergence of biased stochastic gradient methods, while their generalization analysis remains untouched. In this paper, we present the first framework to study the stability and generalization of biased stochastic gradient methods for minimax problems. We establish a

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