3 citations · 7 across the 21 of their papers we have counts for
21 papers · 1 filter
Accelerated Stochastic Gradient Method with Applications to Consensus Problem in Markov-Varying Networks
Vladimir Solodkin, Savelii Chezhegov, Ruslan Nazikov +2
Stochastic optimization is a vital field in the realm of mathematical optimization, finding applications in diverse areas ranging from operations research to machine learning. In t…
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 Methods with Compression for Horizontal and Vertical Federated Learning
Sergey Stanko, Timur Karimullin, Aleksandr Beznosikov +1
Distributed optimization algorithms have emerged as a superior approaches for solving machine learning problems. To accommodate the diverse ways in which data can be stored across…
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…
Accuracy Certificates for Convex Minimization with Inexact Oracle
Egor Gladin, Alexander Gasnikov, Pavel Dvurechensky
Accuracy certificates for convex minimization problems allow for online verification of the accuracy of approximate solutions and provide a theoretically valid online stopping crit…
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…