4 citations · 4 across the 4 of their papers we have counts for
4 papers
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…
Reconstruction of graph colourings
Yury Demidovich, Yaroslav Panichkin, Maksim Zhukovskii
A -deck of a (coloured) graph is a multiset of its induced -vertex subgraphs. Given a graph , when is it possible to reconstruct with high probability a uniformly random c…
A Guide Through the Zoo of Biased SGD
Yury Demidovich, Grigory Malinovsky, Igor Sokolov +1
Stochastic Gradient Descent (SGD) is arguably the most important single algorithm in modern machine learning. Although SGD with unbiased gradient estimators has been studied extens…