7 citations · 13 across the 6 of their papers we have counts for
8 papers
The Stochastic Multi-Proximal Method for Nonsmooth Optimization
Laurent Condat, Elnur Gasanov, Peter Richtárik
Stochastic gradient descent type methods are ubiquitous in machine learning, but they are only applicable to the optimization of differentiable functions. Proximal algorithms are m…
Speeding up Stochastic Proximal Optimization in the High Hessian Dissimilarity Setting
Elnur Gasanov, Peter Richtárik
Stochastic proximal point methods have recently garnered renewed attention within the optimization community, primarily due to their desirable theoretical properties. Notably, thes…
Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants
Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko
Error Feedback (EF) is a highly popular and immensely effective mechanism for fixing convergence issues which arise in distributed training methods (such as distributed GD or SGD)…
Adaptive Compression for Communication-Efficient Distributed Training
Maksim Makarenko, Elnur Gasanov, Rustem Islamov +2
We propose Adaptive Compressed Gradient Descent (AdaCGD) - a novel optimization algorithm for communication-efficient training of supervised machine learning models with adaptive c…
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation
Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin +3
We propose and study a new class of gradient communication mechanisms for communication-efficient training -- three point compressors (3PC) -- as well as efficient distributed nonc…
Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks
Dmitry Kovalev, Elnur Gasanov, Peter Richtárik +1
We consider the task of minimizing the sum of smooth and strongly convex functions stored in a decentralized manner across the nodes of a communication network whose links are allo…