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Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization
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Date
2024Type
- Conference Paper
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Abstract
This paper takes an initial step to systematically investigate the generalization bounds of algorithms for solving nonconvex-(strongly)-concave (NC-SC/NC-C) stochastic minimax optimization measured by the stationarity of primal functions. We first establish algorithm-agnostic generalization bounds via uniform convergence between the empirical minimax problem and the population minimax problem. The sample complexities for achieving $ε$-generalization are $\tilde{\mathcal{O}}(dκ^2ε^{-2})$ and $\tilde{\mathcal{O}}(dε^{-4})$ for NC-SC and NC-C settings, respectively, where $d$ is the dimension and $κ$ is the condition number. We further study the algorithm-dependent generalization bounds via stability arguments of algorithms. In particular, we introduce a novel stability notion for minimax problems and build a connection between generalization bounds and the stability notion. As a result, we establish algorithm-dependent generalization bounds for stochastic gradient descent ascent (SGDA) algorithm and the more general sampling-determined algorithms. Show more
Publication status
acceptedEvent
Subject
Optimization and Control (math.OC); Machine Learning (cs.LG); Machine Learning (stat.ML); FOS: Mathematics; FOS: Computer and information sciencesOrganisational unit
09729 - He, Niao / He, Niao
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Is new version of: https://doi.org/10.48550/arXiv.2205.14278
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