Marina Sheshukova — accepted AI-conference papers

2025

Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson–Romberg Extrapolation

Marina Sheshukova, Denis Belomestny, Alain Oliviero Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov

ICLR 2025poster

We address the problem of solving strongly convex and smooth minimization problems using stochastic gradient descent (SGD) algorithm with a constant step size. Previous works suggested to combine the Polyak-Ruppert averaging procedure with the Richardson-Romberg extrapolation to reduce the asymptot…

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2025

Statistical inference for Linear Stochastic Approximation with Markovian Noise

Sergey Samsonov, Marina Sheshukova, Eric Moulines, Alexey Naumov

NeurIPS 2025poster

In this paper we derive non-asymptotic Berry–Esseen bounds for Polyak–Ruppert averaged iterates of the Linear Stochastic Approximation (LSA) algorithm driven by the Markovian noise. Our analysis yields $O(n^{-1/4})$ convergence rates to the Gaussian limit in the Kolmogorov distance. We further estab…

Cited by 0 Source ↗Scholar ↗

2023

First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities

Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova, Alexander Gasnikov, Alexey Naumov, Eric Moulines

NeurIPS 2023poster

This paper delves into stochastic optimization problems that involve Markovian noise. We present a unified approach for the theoretical analysis of first-order gradient methods for stochastic optimization and variational inequalities. Our approach covers scenarios for both non-convex and strongly co…

Cited by 22 Source ↗PDF ↗Scholar ↗