16 citations · 36 across the 20 of their papers we have counts for
22 papers
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…
Power of Generalized Smoothness in Stochastic Convex Optimization: First- and Zero-Order Algorithms
Aleksandr Lobanov, Alexander Gasnikov
This paper is devoted to the study of stochastic optimization problems under the generalized smoothness assumption. By considering the unbiased gradient oracle in Stochastic Gradie…
Linear Convergence Rate in Convex Setup is Possible! Gradient Descent Method Variants under -Smoothness
Aleksandr Lobanov, Alexander Gasnikov, Eduard Gorbunov +1
The gradient descent (GD) method -- is a fundamental and likely the most popular optimization algorithm in machine learning (ML), with a history traced back to a paper in 1847 (Cau…
Accelerated zero-order SGD under high-order smoothness and overparameterized regime
Georgii Bychkov, Darina Dvinskikh, Anastasia Antsiferova +2
We present a novel gradient-free algorithm to solve a convex stochastic optimization problem, such as those encountered in medicine, physics, and machine learning (e.g., adversaria…
Nesterov's method of dichotomy via Order Oracle: The problem of optimizing a two-variable function on a square
Boris Chervonenkis, Andrei Krasnov, Alexander Gasnikov +1
The challenges of black box optimization arise due to imprecise responses and limited output information. This article describes new results on optimizing multivariable functions u…
Improved Iteration Complexity in Black-Box Optimization Problems under Higher Order Smoothness Function Condition
Aleksandr Lobanov
This paper is devoted to the study (common in many applications) of the black-box optimization problem, where the black-box represents a gradient-free oracle …
Gradient-free algorithm for saddle point problems under overparametrization
Ekaterina Statkevich, Sofiya Bondar, Darina Dvinskikh +2
This paper focuses on solving a stochastic saddle point problem (SPP) under an overparameterized regime for the case, when the gradient computation is impractical. As an intermedia…
Acceleration Exists! Optimization Problems When Oracle Can Only Compare Objective Function Values
Aleksandr Lobanov, Alexander Gasnikov, Andrei Krasnov
Frequently, the burgeoning field of black-box optimization encounters challenges due to a limited understanding of the mechanisms of the objective function. To address such problem…
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…
About some works of Boris Polyak on convergence of gradient methods and their development
Seydamet Ablaev, Aleksandr Beznosikov, Alexander Gasnikov +4
The paper presents a review of the state-of-the-art of subgradient and accelerated methods of convex optimization, including in the presence of disturbances and access to various i…
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…
The Black-Box Optimization Problem: Zero-Order Accelerated Stochastic Method via Kernel Approximation
Aleksandr Lobanov, Nail Bashirov, Alexander Gasnikov
In this paper, we study the standard formulation of an optimization problem when the computation of gradient is not available. Such a problem can be classified as a "black box" opt…
Accelerated Zero-Order SGD Method for Solving the Black Box Optimization Problem under "Overparametrization" Condition
Aleksandr Lobanov, Alexander Gasnikov
This paper is devoted to solving a convex stochastic optimization problem in a overparameterization setup for the case where the original gradient computation is not available, but…
Non-Smooth Setting of Stochastic Decentralized Convex Optimization Problem Over Time-Varying Graphs
Aleksandr Lobanov, Andrew Veprikov, Georgiy Konin +3
Distributed optimization has a rich history. It has demonstrated its effectiveness in many machine learning applications, etc. In this paper we study a subclass of distributed opti…
Highly Smoothness Zero-Order Methods for Solving Optimization Problems under PL Condition
Aleksandr Lobanov, Alexander Gasnikov, Fedor Stonyakin
In this paper, we study the black box optimization problem under the Polyak--Lojasiewicz (PL) condition, assuming that the objective function is not just smooth, but has higher smo…
Stochastic Adversarial Noise in the "Black Box" Optimization Problem
Aleksandr Lobanov
This paper is devoted to the study of the solution of a stochastic convex black box optimization problem. Where the black box problem means that the gradient-free oracle only retur…
Zero-Order Stochastic Conditional Gradient Sliding Method for Non-smooth Convex Optimization
Aleksandr Lobanov, Anton Anikin, Alexander Gasnikov +2
The conditional gradient idea proposed by Marguerite Frank and Philip Wolfe in 1956 was so well received by the community that new algorithms (also called Frank--Wolfe type algorit…
Randomized gradient-free methods in convex optimization
Alexander Gasnikov, Darina Dvinskikh, Pavel Dvurechensky +3
This review presents modern gradient-free methods to solve convex optimization problems. By gradient-free methods, we mean those that use only (noisy) realizations of the objective…
Gradient-Free Federated Learning Methods with and -Randomization for Non-Smooth Convex Stochastic Optimization Problems
Aleksandr Lobanov, Belal Alashqar, Darina Dvinskikh +1
This paper studies non-smooth problems of convex stochastic optimization. Using the smoothing technique based on the replacement of the function value at the considered point by th…