4 papers
Do We Need Asynchronous SGD? On the Near-Optimality of Synchronous Methods
Grigory Begunov, Alexander Tyurin
Modern distributed optimization methods mostly rely on traditional synchronous approaches, despite substantial recent progress in asynchronous optimization. We revisit Synchronous…
Local SGD and Federated Averaging Through the Lens of Time Complexity
Adrien Fradin, Peter Richtárik, Alexander Tyurin
We revisit the classical Local SGD and Federated Averaging (FedAvg) methods for distributed optimization and federated learning. While prior work has primarily focused on iteration…
From Logistic Regression to the Perceptron Algorithm: Exploring Gradient Descent with Large Step Sizes
Alexander Tyurin
We focus on the classification problem with a separable dataset, one of the most important and classical problems from machine learning. The standard approach to this task is logis…
Adaptive fast gradient method in stochastic optimization tasks
Alexander Tyurin
In this paper, we describe a stochastic adaptive fast gradient descent method based on the mirror variant of similar triangles method. To our knowledge, this is the first attempt t…