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cs.LG2024
Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration
Tao Sun, Xinwang Liu, Kun Yuan
Gradient clipping has long been considered essential for ensuring the convergence of Stochastic Gradient Descent (SGD) in the presence of heavy-tailed gradient noise. In this paper…
cs.LG2024
Stability and Generalization for Stochastic Recursive Momentum-based Algorithms for (Strongly-)Convex One to -Level Stochastic Optimizations
Xiaokang Pan, Xingyu Li, Jin Liu +4
STOchastic Recursive Momentum (STORM)-based algorithms have been widely developed to solve one to -level () stochastic optimization problems. Specifically, they use es…