3 citations · 6 across the 5 of their papers we have counts for
5 papers
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…
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…
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…