Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise and H\"{o}lder Smoothness
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arXiv:2609.12785v1 Announce Type: cross Abstract: Classical convergence guarantees for stochastic gradient methods typically assume Lipschitz-smooth objectives and finite-variance gradient noise, both frequently violated in practice. In contrast, we study nonconvex stochastic optimization under the…
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- 2026-09-14 04:00 · arXiv stat.ML
Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise and H\"{o}lder Smoothness