3 citations · 5 across the 8 of their papers we have counts for
6 papers · 1 filter
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 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…
Intermediate Gradient Methods with Relative Inexactness
Nikita Kornilov, Eduard Gorbunov, Mohammad Alkousa +3
This paper is devoted to first-order algorithms for smooth convex optimization with inexact gradients. Unlike the majority of the literature on this topic, we consider the setting…
Unified analysis of SGD-type methods
Eduard Gorbunov
This note focuses on a simple approach to the unified analysis of SGD-type methods from (Gorbunov et al., 2020) for strongly convex smooth optimization problems. The similarities i…
Byzantine-Robust Loopless Stochastic Variance-Reduced Gradient
Nikita Fedin, Eduard Gorbunov
Distributed optimization with open collaboration is a popular field since it provides an opportunity for small groups/companies/universities, and individuals to jointly solve huge-…
Distributed and Stochastic Optimization Methods with Gradient Compression and Local Steps
Eduard Gorbunov
In this thesis, we propose new theoretical frameworks for the analysis of stochastic and distributed methods with error compensation and local updates. Using these frameworks, we d…