1 citations · 1 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
Revisiting Stochastic Proximal Point Methods: Generalized Smoothness and Similarity
Zhirayr Tovmasyan, Grigory Malinovsky, Laurent Condat +1
The growing prevalence of nonsmooth optimization problems in machine learning has spurred significant interest in generalized smoothness assumptions. Among these, the (L0, L1)-smoo…
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…
Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization
Soumia Boucherouite, Grigory Malinovsky, Peter Richtárik +1
In this paper, we propose a new zero order optimization method called minibatch stochastic three points (MiSTP) method to solve an unconstrained minimization problem in a setting w…