Grigory Malinovsky

According to our database, Grigory Malinovsky authored at least 21 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation.

Grigory Malinovsky, Umberto Michieli, Hasan Abed Al Kader Hammoud, Taha Ceritli, Hayder Elesedy, Mete Ozay, Peter Richtárik

CoRR, 2024

MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence.

Ionut-Vlad Modoranu, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic, Thomas Robert, Peter Richtárik, Dan Alistarh

CoRR, 2024

Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction.

Yury Demidovich, Grigory Malinovsky, Peter Richtárik

CoRR, 2024

Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization.

Soumia Boucherouite, Grigory Malinovsky, Peter Richtárik, El Houcine Bergou

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

MAST: Model-Agnostic Sparsified Training.

Yury Demidovich, Grigory Malinovsky, Egor Shulgin, Peter Richtárik

CoRR, 2023

Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously: Just Clip Gradient Differences.

Grigory Malinovsky, Peter Richtárik, Samuel Horváth, Eduard Gorbunov

CoRR, 2023

Improving Accelerated Federated Learning with Compression and Importance Sampling.

Michal Grudzien, Grigory Malinovsky, Peter Richtárik

CoRR, 2023

TAMUNA: Accelerated Federated Learning with Local Training and Partial Participation.

Laurent Condat, Grigory Malinovsky, Peter Richtárik

CoRR, 2023

Federated Learning with Regularized Client Participation.

Grigory Malinovsky, Samuel Horváth, Konstantin Burlachenko, Peter Richtárik

CoRR, 2023

Random Reshuffling with Variance Reduction: New Analysis and Better Rates.

Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik

Proceedings of the Uncertainty in Artificial Intelligence, 2023

A Guide Through the Zoo of Biased SGD.

Yury Demidovich, Grigory Malinovsky, Igor Sokolov, Peter Richtárik

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization.

Grigory Malinovsky, Konstantin Mishchenko, Peter Richtárik

Proceedings of the 4th International Workshop on Distributed Machine Learning, 2023

Can 5th Generation Local Training Methods Support Client Sampling? Yes!

Michal Grudzien, Grigory Malinovsky, Peter Richtárik

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

An Optimal Algorithm for Strongly Convex Min-min Optimization.

Dmitry Kovalev, Alexander V. Gasnikov, Grigory Malinovsky

CoRR, 2022

Can 5^th Generation Local Training Methods Support Client Sampling? Yes!

Michal Grudzien, Grigory Malinovsky, Peter Richtárik

CoRR, 2022

Federated Optimization Algorithms with Random Reshuffling and Gradient Compression.

Abdurakhmon Sadiev, Grigory Malinovsky, Eduard Gorbunov, Igor Sokolov, Ahmed Khaled, Konstantin Burlachenko, Peter Richtárik

CoRR, 2022

Federated Random Reshuffling with Compression and Variance Reduction.

Grigory Malinovsky, Peter Richtárik

CoRR, 2022

Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning.

Grigory Malinovsky, Kai Yi, Peter Richtárik

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!

Konstantin Mishchenko, Grigory Malinovsky, Sebastian U. Stich, Peter Richtárik

Proceedings of the International Conference on Machine Learning, 2022

2020

Distributed Proximal Splitting Algorithms with Rates and Acceleration.

Laurent Condat, Grigory Malinovsky, Peter Richtárik

CoRR, 2020

From Local SGD to Local Fixed-Point Methods for Federated Learning.

Grigory Malinovskiy, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtárik

Proceedings of the 37th International Conference on Machine Learning, 2020