Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise and H\"{o}lder Smoothness
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 joint relaxation of these assumptions: objectives with $(L,s)$-H\"olde...
M. Zaman, Anirbit Mukherjee
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