New in-expectation convergence guarantees for SMD, ASMD (convex) and SGD, SGDM (nonconvex) under heavy-tailed noise without bounded-domain restrictions or algorithmic modifications.
A unified framework for bregman proximal methods: subgradient, gradient, and accelerated gradient schemes
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In-Expectation Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise
New in-expectation convergence guarantees for SMD, ASMD (convex) and SGD, SGDM (nonconvex) under heavy-tailed noise without bounded-domain restrictions or algorithmic modifications.