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ORCIDGrigory Malinovsky
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2020 – today
- 2024
[c8]Soumia Boucherouite, Grigory Malinovsky, Peter Richtárik
, El Houcine Bergou:
Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization. AAAI 2024: 20344-20352- [i20]Yury Demidovich, Grigory Malinovsky, Peter Richtárik:
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction. CoRR abs/2403.06677 (2024) - [i19]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 abs/2405.15593 (2024) - [i18]Grigory Malinovsky, Umberto Michieli, Hasan Abed Al Kader Hammoud, Taha Ceritli, Hayder Elesedy, Mete Ozay, Peter Richtárik:
Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation. CoRR abs/2410.08305 (2024) - 2023
- [c7]Michal Grudzien, Grigory Malinovsky, Peter Richtárik:
Can 5th Generation Local Training Methods Support Client Sampling? Yes! AISTATS 2023: 1055-1092 - [c6]Grigory Malinovsky, Konstantin Mishchenko, Peter Richtárik:
Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization. DistributedML@CoNEXT 2023: 85-104 - [c5]Yury Demidovich, Grigory Malinovsky, Igor Sokolov, Peter Richtárik:
A Guide Through the Zoo of Biased SGD. NeurIPS 2023 - [c4]Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik:
Random Reshuffling with Variance Reduction: New Analysis and Better Rates. UAI 2023: 1347-1357 - [i17]Grigory Malinovsky, Samuel Horváth, Konstantin Burlachenko, Peter Richtárik:
Federated Learning with Regularized Client Participation. CoRR abs/2302.03662 (2023) - [i16]Laurent Condat, Grigory Malinovsky, Peter Richtárik:
TAMUNA: Accelerated Federated Learning with Local Training and Partial Participation. CoRR abs/2302.09832 (2023) - [i15]Yury Demidovich, Grigory Malinovsky, Igor Sokolov, Peter Richtárik:
A Guide Through the Zoo of Biased SGD. CoRR abs/2305.16296 (2023) - [i14]Michal Grudzien, Grigory Malinovsky, Peter Richtárik:
Improving Accelerated Federated Learning with Compression and Importance Sampling. CoRR abs/2306.03240 (2023) - [i13]Grigory Malinovsky, Peter Richtárik, Samuel Horváth, Eduard Gorbunov:
Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously: Just Clip Gradient Differences. CoRR abs/2311.14127 (2023) - [i12]Yury Demidovich, Grigory Malinovsky, Egor Shulgin, Peter Richtárik:
MAST: Model-Agnostic Sparsified Training. CoRR abs/2311.16086 (2023) - 2022
- [c3]Konstantin Mishchenko, Grigory Malinovsky, Sebastian U. Stich, Peter Richtárik:
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! ICML 2022: 15750-15769 - [c2]Grigory Malinovsky, Kai Yi, Peter Richtárik:
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. NeurIPS 2022 - [i11]Grigory Malinovsky, Konstantin Mishchenko, Peter Richtárik:
Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization. CoRR abs/2201.11066 (2022) - [i10]Konstantin Mishchenko, Grigory Malinovsky, Sebastian U. Stich, Peter Richtárik:
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! CoRR abs/2202.09357 (2022) - [i9]Grigory Malinovsky, Peter Richtárik:
Federated Random Reshuffling with Compression and Variance Reduction. CoRR abs/2205.03914 (2022) - [i8]Abdurakhmon Sadiev, Grigory Malinovsky, Eduard Gorbunov, Igor Sokolov, Ahmed Khaled, Konstantin Burlachenko, Peter Richtárik:
Federated Optimization Algorithms with Random Reshuffling and Gradient Compression. CoRR abs/2206.07021 (2022) - [i7]Grigory Malinovsky, Kai Yi, Peter Richtárik:
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. CoRR abs/2207.04338 (2022) - [i6]Soumia Boucherouite, Grigory Malinovsky, Peter Richtárik, El Houcine Bergou:
Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization. CoRR abs/2209.07883 (2022) - [i5]Michal Grudzien, Grigory Malinovsky, Peter Richtárik:
Can 5th Generation Local Training Methods Support Client Sampling? Yes! CoRR abs/2212.14370 (2022) - [i4]Dmitry Kovalev, Alexander V. Gasnikov, Grigory Malinovsky:
An Optimal Algorithm for Strongly Convex Min-min Optimization. CoRR abs/2212.14439 (2022) - 2021
- [i3]Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik:
Random Reshuffling with Variance Reduction: New Analysis and Better Rates. CoRR abs/2104.09342 (2021) - 2020
- [c1]Grigory Malinovskiy, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtárik:
From Local SGD to Local Fixed-Point Methods for Federated Learning. ICML 2020: 6692-6701 - [i2]Grigory Malinovsky, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtárik:
From Local SGD to Local Fixed Point Methods for Federated Learning. CoRR abs/2004.01442 (2020) - [i1]Laurent Condat, Grigory Malinovsky, Peter Richtárik:
Distributed Proximal Splitting Algorithms with Rates and Acceleration. CoRR abs/2010.00952 (2020)
Coauthor Index
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