Abdurakhmon Sadiev

According to our database, Abdurakhmon Sadiev authored at least 23 papers between 2020 and 2026.

Collaborative distances:

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

2026

Ringmaster LMO: Asynchronous Linear Minimization Oracle Momentum Method.

Abdurakhmon Sadiev, Artavazd Maranjyan, Ivan Ilin, Peter Richtárik

CoRR, May, 2026

2025

Better LMO-based Momentum Methods with Second-Order Information.

Sarit Khirirat, Abdurakhmon Sadiev, Yury Demidovich, Peter Richtárik

CoRR, December, 2025

Improved Convergence in Parameter-Agnostic Error Feedback through Momentum.

Abdurakhmon Sadiev, Yury Demidovich, Igor Sokolov, Grigory Malinovsky, Sarit Khirirat, Peter Richtárik

CoRR, November, 2025

Bernoulli-LoRA: A Theoretical Framework for Randomized Low-Rank Adaptation.

Igor Sokolov, Abdurakhmon Sadiev, Yury Demidovich, Fawaz S. Al-Qahtani, Peter Richtárik

CoRR, August, 2025

Second-order Optimization under Heavy-Tailed Noise: Hessian Clipping and Sample Complexity Limits.

Abdurakhmon Sadiev, Peter Richtárik, Ilyas Fatkhullin

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Error Feedback under (L₀, L₁)-Smoothness: Normalization and Momentum.

Sarit Khirirat, Abdurakhmon Sadiev, Artem Riabinin, Eduard Gorbunov, Peter Richtárik

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

2024

Stochastic Gradient Methods with Preconditioned Updates.

Abdurakhmon Sadiev, Aleksandr Beznosikov, Abdulla Jasem Almansoori, Dmitry Kamzolov, Rachael Tappenden, Martin Takác

J. Optim. Theory Appl., May, 2024

Differentially Private Random Block Coordinate Descent.

Artavazd Maranjyan, Abdurakhmon Sadiev, Peter Richtárik

CoRR, 2024

SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning.

Avetik G. Karagulyan, Egor Shulgin, Abdurakhmon Sadiev, Peter Richtárik

CoRR, 2024

A Unified Theory of Stochastic Proximal Point Methods without Smoothness.

Peter Richtárik, Abdurakhmon Sadiev, Yury Demidovich

CoRR, 2024

Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences.

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

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise.

Eduard Gorbunov, Abdurakhmon Sadiev, Marina Danilova, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods.

Zheng Shi, Abdurakhmon Sadiev, Nicolas Loizou, Peter Richtárik, Martin Takác

Trans. Mach. Learn. Res., 2023

Adaptive Compression for Communication-Efficient Distributed Training.

Maksim Makarenko, Elnur Gasanov, Abdurakhmon Sadiev, Rustem Islamov, Peter Richtárik

Trans. Mach. Learn. Res., 2023

High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance.

Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik

Proceedings of the International Conference on Machine Learning, 2023

2022

Decentralized personalized federated learning: Lower bounds and optimal algorithm for all personalization modes.

Abdurakhmon Sadiev, Ekaterina Borodich, Aleksandr Beznosikov, Darina Dvinskikh, Saveliy Chezhegov, Rachael Tappenden, Martin Takác, Alexander V. Gasnikov

EURO J. Comput. Optim., 2022

Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox.

Abdurakhmon Sadiev, Dmitry Kovalev, 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

Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox.

Abdurakhmon Sadiev, Dmitry Kovalev, Peter Richtárik

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

Optimal Algorithms for Decentralized Stochastic Variational Inequalities.

Dmitry Kovalev, Aleksandr Beznosikov, Abdurakhmon Sadiev, Michael Persiianov, Peter Richtárik, Alexander V. Gasnikov

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

2021

Decentralized Personalized Federated Min-Max Problems.

Aleksandr Beznosikov, Vadim Sushko, Abdurakhmon Sadiev, Alexander V. Gasnikov

CoRR, 2021

2020

Zeroth-Order Algorithms for Smooth Saddle-Point Problems.

Abdurakhmon Sadiev, Aleksandr Beznosikov, Pavel E. Dvurechensky, Alexander V. Gasnikov

CoRR, 2020

Gradient-Free Methods for Saddle-Point Problem.

Aleksandr Beznosikov, Abdurakhmon Sadiev, Alexander V. Gasnikov

CoRR, 2020