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Accelerated Stochastic Method under $(H_0,H_1)$-Smoothness and Heavy-Tailed Noise
We develop an accelerated stochastic method for convex $(H_0,H_1)$-smooth optimization under heavy-tailed noise. The unbiased oracle has a finite $p$-th noise moment, $1<p\le2$, with constant, gradient-dependent, and gap-dependent terms. Our method combines accelerated updates with clipping, project
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