4 citations · 4 across the 10 of their papers we have counts for
4 papers · 1 filter
Better LMO-based Momentum Methods with Second-Order Information
Sarit Khirirat, Abdurakhmon Sadiev, Yury Demidovich +1
The use of momentum in stochastic optimization algorithms has shown empirical success across a range of machine learning tasks. Recently, a new class of stochastic momentum algorit…
Improved Convergence in Parameter-Agnostic Error Feedback through Momentum
Abdurakhmon Sadiev, Yury Demidovich, Igor Sokolov +3
Communication compression is essential for scalable distributed training of modern machine learning models, but it often degrades convergence due to the noise it introduces. Error…
Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization
Yury Demidovich, Petr Ostroukhov, Grigory Malinovsky +4
Non-convex Machine Learning problems typically do not adhere to the standard smoothness assumption. Based on empirical findings, Zhang et al. (2020b) proposed a more realistic gene…
A Unified Theory of Stochastic Proximal Point Methods without Smoothness
Peter Richtárik, Abdurakhmon Sadiev, Yury Demidovich
This paper presents a comprehensive analysis of a broad range of variations of the stochastic proximal point method (SPPM). Proximal point methods have attracted considerable inter…