Generalization Bounds for Stochastic Saddle Point Problems
Junyu Zhang,
Mingyi Hong,
Mengdi Wang
et al.
Abstract:This paper studies the generalization bounds for the empirical saddle point (ESP) solution to stochastic saddle point (SSP) problems. For SSP with Lipschitz continuous and strongly convex-strongly concave objective functions, we establish an O (1/n) generalization bound by using a uniform stability argument. We also provide generalization bounds under a variety of assumptions, including the cases without strong convexity and without bounded domains. We illustrate our results in two examples: batch policy learn… Show more
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