16 citations · 36 across the 20 of their papers we have counts for
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Accelerated Stochastic Method under -Smoothness and Heavy-Tailed Noise
Aleksandr Lobanov, Darina Dvinskikh, Alexander Gasnikov
We develop an accelerated stochastic method for convex -smooth optimization under heavy-tailed noise. The unbiased oracle has a finite -th noise moment, , wi…
Localize, Restart, Accelerate: Stochastic Optimization under Generalized Smoothness
Darina Dvinskikh, Alexander Gasnikov, Aleksandr Lobanov +1
We study stochastic convex optimization under asymmetric \((L_0,L_1)\)-generalized smoothness, a model motivated by machine-learning objectives whose local curvature may grow with…
A Few Accelerated Algorithms for Convex Optimization under -Smoothness
Aleksandr Lobanov
We develop accelerated algorithms for convex -smooth optimization, where . This class generalizes standard smoothness and contain…
Avoiding Bias in Clipped SGD for Overparameterized Models under Generalized Smoothness
Aleksandr Lobanov, Anastasia Koloskova
Modern machine learning is dominated by complex, overparameterized architectures capable of interpolating data and achieving zero training loss. For such models, we investigate the…