13 citations · 57 across the 29 of their papers we have counts for
7 papers · 1 filter
An Optimal Algorithm for Strongly Convex Min-min Optimization
Alexander Gasnikov, Dmitry Kovalev, Grigory Malinovsky
In this paper we study the smooth strongly convex minimization problem . The existing optimal first-order methods require $\mathcal{O}(\sqrt{\max\{κ_x,κ_y\}}…
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…
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning
Grigory Malinovsky, Kai Yi, Peter Richtárik
We study distributed optimization methods based on the {\em local training (LT)} paradigm: achieving communication efficiency by performing richer local gradient-based training on…
Federated Optimization Algorithms with Random Reshuffling and Gradient Compression
Abdurakhmon Sadiev, Grigory Malinovsky, Eduard Gorbunov +4
Gradient compression is a popular technique for improving communication complexity of stochastic first-order methods in distributed training of machine learning models. However, th…
Federated Random Reshuffling with Compression and Variance Reduction
Grigory Malinovsky, Peter Richtárik
Random Reshuffling (RR), which is a variant of Stochastic Gradient Descent (SGD) employing sampling without replacement, is an immensely popular method for training supervised mach…
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!
Konstantin Mishchenko, Grigory Malinovsky, Sebastian Stich +1
We introduce ProxSkip -- a surprisingly simple and provably efficient method for minimizing the sum of a smooth () and an expensive nonsmooth proximable () function. The cano…