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The Role of Gradient Modification in Heavy-Tailed Nonconvex Stochastic Min-Max Optimization
Stochastic min-max optimization has attracted increasing attention due to its applications in modern machine learning, while existing theoretical studies mainly rely on the bounded variance assumption for stochastic gradients. Under heavy-tailed noise, where stochastic gradients only possess a finit
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