activity
20232026
most citedAccelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance

3 citations · 4 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG2026

LionMuon: Alternating Spectral and Sign Descent for Efficient Training

Arman Bolatov, Artem Riabinin, Nikita Kornilov +4

In large-scale optimization, the cheapness and effectiveness of update steps are the most crucial factors for a successful optimizer. Sign-based optimizers like Lion or Signum prod…

math.OC2025

On Solving Minimization and Min-Max Problems by First-Order Methods with Relative Error in Gradients

Artem Vasin, Valery Krivchenko, Dmitry Kovalev +6

First-order methods for minimization and saddle point (min-max) problems are widely used for solving large-scale problems, in particular arising in machine learning. The majority o…

math.OC2025

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under -Smoothness

Nikita Kornilov, Philip Zmushko, Andrei Semenov +3

In recent years, non-convex optimization problems are more often described by generalized -smoothness assumption rather than standard one. Meanwhile, severely corrupted…

stat.ML2024

Optimal Flow Matching: Learning Straight Trajectories in Just One Step

Nikita Kornilov, Petr Mokrov, Alexander Gasnikov +1

Over the several recent years, there has been a boom in development of Flow Matching (FM) methods for generative modeling. One intriguing property pursued by the community is the a…

math.OC2024

Median Clipping for Zeroth-order Non-Smooth Convex Optimization and Multi-Armed Bandit Problem with Heavy-tailed Symmetric Noise

Nikita Kornilov, Yuriy Dorn, Aleksandr Lobanov +5

In this paper, we consider non-smooth convex optimization with a zeroth-order oracle corrupted by symmetric stochastic noise. Unlike the existing high-probability results requiring…

math.OC2023★ 3 cited

Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance

Nikita Kornilov, Ohad Shamir, Aleksandr Lobanov +5

In this paper, we consider non-smooth stochastic convex optimization with two function evaluations per round under infinite noise variance. In the classical setting when noise has…