← Search

Aleksandr Shestakov

2 accepted papers

2026

From Optimization to Generalization under Heavy-Tailed Data: The Role of Gradient Clipping

ICML 2026poster

Gradient clipping is widely used to stabilize stochastic gradient methods and is often theoretically motivated by heavy-tailed gradient noise, where even second moments may be infinite, seemingly contradicting empirical risk minimization where all moments are finite for a fixed dataset. We resolve t…

Cited by 0SourceScholar
2024

Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases

AISTATS 2024poster

The Conditional Gradient (or Frank-Wolfe) method is one of the most well-known methods for solving constrained optimization problems appearing in various machine learning tasks. The simplicity of iteration and applicability to many practical problems helped the method to gain popularity in the commu…

Cited by 4SourcePDFScholar