4 citations · 4 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction
Yury Demidovich, Grigory Malinovsky, Peter Richtárik
In this study, we investigate stochastic optimization on Riemannian manifolds, focusing on the crucial variance reduction mechanism used in both Euclidean and Riemannian settings.…
Correlated Quantization for Faster Nonconvex Distributed Optimization
Andrei Panferov, Yury Demidovich, Ahmad Rammal +1
Quantization (Alistarh et al., 2017) is an important (stochastic) compression technique that reduces the volume of transmitted bits during each communication round in distributed m…