Grigory Malinovsky

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 26

Top Topics

Variance Reduction

Optimization Algorithm

Top Venues

DistributedML@CoNEXT

Publications

Yury Demidovich, Grigory Malinovsky, Peter Richtárik
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction. CoRR (2024)
Ionut-Vlad Modoranu, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic, Thomas Robert, Peter Richtárik, Dan Alistarh
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence. CoRR (2024)
Soumia Boucherouite, Grigory Malinovsky, Peter Richtárik, El Houcine Bergou
Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization. AAAI (2024)
Yury Demidovich, Grigory Malinovsky, Igor Sokolov, Peter Richtárik
A Guide Through the Zoo of Biased SGD. NeurIPS (2023)
Michal Grudzien, Grigory Malinovsky, Peter Richtárik
Improving Accelerated Federated Learning with Compression and Importance Sampling. CoRR (2023)
Grigory Malinovsky, Peter Richtárik, Samuel Horváth, Eduard Gorbunov
Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously: Just Clip Gradient Differences. CoRR (2023)
Laurent Condat, Grigory Malinovsky, Peter Richtárik
TAMUNA: Accelerated Federated Learning with Local Training and Partial Participation. CoRR (2023)
Yury Demidovich, Grigory Malinovsky, Igor Sokolov, Peter Richtárik
A Guide Through the Zoo of Biased SGD. CoRR (2023)
Yury Demidovich, Grigory Malinovsky, Egor Shulgin, Peter Richtárik
MAST: Model-Agnostic Sparsified Training. CoRR (2023)
Michal Grudzien, Grigory Malinovsky, Peter Richtárik
Can 5th Generation Local Training Methods Support Client Sampling? Yes! AISTATS (2023)
Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik
Random Reshuffling with Variance Reduction: New Analysis and Better Rates. UAI (2023)
Grigory Malinovsky, Konstantin Mishchenko, Peter Richtárik
Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization. DistributedML@CoNEXT (2023)
Grigory Malinovsky, Samuel Horváth, Konstantin Burlachenko, Peter Richtárik
Federated Learning with Regularized Client Participation. CoRR (2023)
Grigory Malinovsky, Kai Yi, Peter Richtárik
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. NeurIPS (2022)
Dmitry Kovalev, Alexander V. Gasnikov, Grigory Malinovsky
An Optimal Algorithm for Strongly Convex Min-min Optimization. CoRR (2022)
Soumia Boucherouite, Grigory Malinovsky, Peter Richtárik, El Houcine Bergou
Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization. CoRR (2022)
Konstantin Mishchenko, Grigory Malinovsky, Sebastian Stich, Peter Richtárik
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! CoRR (2022)
Grigory Malinovsky, Kai Yi, Peter Richtárik
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. CoRR (2022)
Michal Grudzien, Grigory Malinovsky, Peter Richtárik
Generation Local Training Methods Support Client Sampling? Yes! CoRR (2022)
Konstantin Mishchenko, Grigory Malinovsky, Sebastian Stich, Peter Richtárik
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! ICML (2022)
Grigory Malinovsky, Konstantin Mishchenko, Peter Richtárik
Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization. CoRR (2022)
Abdurakhmon Sadiev, Grigory Malinovsky, Eduard Gorbunov, Igor Sokolov, Ahmed Khaled, Konstantin Burlachenko, Peter Richtárik
Federated Optimization Algorithms with Random Reshuffling and Gradient Compression. CoRR (2022)
Grigory Malinovsky, Peter Richtárik
Federated Random Reshuffling with Compression and Variance Reduction. CoRR (2022)
Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik
Random Reshuffling with Variance Reduction: New Analysis and Better Rates. CoRR (2021)
Grigory Malinovsky, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtárik
From Local SGD to Local Fixed Point Methods for Federated Learning. CoRR (2020)
Laurent Condat, Grigory Malinovsky, Peter Richtárik
Distributed Proximal Splitting Algorithms with Rates and Acceleration. CoRR (2020)