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math.OC2024★ 2 cited
From Gradient Clipping to Normalization for Heavy Tailed SGD
Florian Hübler, Ilyas Fatkhullin, Niao He
Recent empirical evidence indicates that many machine learning applications involve heavy-tailed gradient noise, which challenges the standard assumptions of bounded variance in st…
math.OC2024
Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence
Ilyas Fatkhullin, Niao He
This paper revisits the convergence of Stochastic Mirror Descent (SMD) in the contemporary nonconvex optimization setting. Existing results for batch-free nonconvex SMD restrict th…
math.OC2024★ 1 cited
Stochastic Optimization under Hidden Convexity
Ilyas Fatkhullin, Niao He, Yifan Hu
In this work, we consider constrained stochastic optimization problems under hidden convexity, i.e., those that admit a convex reformulation via non-linear (but invertible) map $c(…