Aleksandr Beznosikov

According to our database, Aleksandr Beznosikov authored at least 56 papers between 2020 and 2025.

Collaborative distances:

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

2025

Enhancing Stability of Physics-Informed Neural Network Training Through Saddle-Point Reformulation.

Dmitry Bylinkin, Mikhail Aleksandrov, Saveliy Chezhegov, Aleksandr Beznosikov

CoRR, July, 2025

Leveraging Coordinate Momentum in SignSGD and Muon: Memory-Optimized Zero-Order.

Egor Petrov, Grigoriy Evseev, Aleksey Antonov, Andrey Veprikov, Pavel Plyusnin, Nikolay Bushkov, Stanislav Moiseev, Aleksandr Beznosikov

CoRR, June, 2025

Sign-SGD is the Golden Gate between Multi-Node to Single-Node Learning: Significant Boost via Parameter-Free Optimization.

Daniil Medyakov, Sergey Stanko, Gleb Molodtsov, Philip Zmushko, Grigoriy Evseev, Egor Petrov, Aleksandr Beznosikov

CoRR, June, 2025

WeightLoRA: Keep Only Necessary Adapters.

Andrey Veprikov, Vladimir Solodkin, Alexander Zyl, Andrey Savchenko, Aleksandr Beznosikov

CoRR, June, 2025

Convergence of Clipped-SGD for Convex (L₀,L₁)-Smooth Optimization with Heavy-Tailed Noise.

Saveliy Chezhegov, Aleksandr Beznosikov, Samuel Horváth, Eduard Gorbunov

CoRR, May, 2025

Trial and Trust: Addressing Byzantine Attacks with Comprehensive Defense Strategy.

Gleb Molodtsov, Daniil Medyakov, Sergey Skorik, Nikolas Khachaturov, Shahane Tigranyan, Vladimir Aletov, Aram Avetisyan, Martin Takác, Aleksandr Beznosikov

CoRR, May, 2025

Broadening Discovery through Structural Models: Multimodal Combination of Local and Structural Properties for Predicting Chemical Features.

Nikolai Rekut, Alexey Orlov, Klea Ziu, Elizaveta Starykh, Martin Takác, Aleksandr Beznosikov

CoRR, February, 2025

Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling.

Daniil Medyakov, Gleb Molodtsov, Saveliy Chezhegov, Alexey Rebrikov, Aleksandr Beznosikov

CoRR, February, 2025

Sign Operator for Coping with Heavy-Tailed Noise: High Probability Convergence Bounds with Extensions to Distributed Optimization and Comparison Oracle.

Nikita Kornilov, Philip Zmushko, Andrei Semenov, Alexander V. Gasnikov, Aleksandr Beznosikov

CoRR, February, 2025

When Extragradient Meets PAGE: Bridging Two Giants to Boost Variational Inequalities.

Gleb Molodtsov, Valery Parfenov, Egor Petrov, Grigoriy Evseev, Daniil Medyakov, Aleksandr Beznosikov

Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2025

Accelerated Methods with Compressed Communications for Distributed Optimization Problems Under Data Similarity.

Dmitry Bylinkin, Aleksandr Beznosikov

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024

Preconditioning meets biased compression for efficient distributed optimization.

Vitali Pirau, Aleksandr Beznosikov, Martin Takác, Vladislav Matyukhin, Alexander V. Gasnikov

Comput. Manag. Sci., June, 2024

Decentralized optimization over slowly time-varying graphs: algorithms and lower bounds.

Dmitry Metelev, Aleksandr Beznosikov, Alexander Rogozin, Alexander V. Gasnikov, Anton V. Proskurnikov

Comput. Manag. Sci., June, 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

Random-reshuffled SARAH does not need full gradient computations.

Aleksandr Beznosikov, Martin Takác

Optim. Lett., April, 2024

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training.

Philip Zmushko, Marat Mansurov, Ruslan Svirschevski, Denis Kuznedelev, Max Ryabinin, Aleksandr Beznosikov

CoRR, 2024

Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learning.

Andrei Semenov, Philip Zmushko, Alexander Pichugin, Aleksandr Beznosikov

CoRR, 2024

FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training.

Philip Zmushko, Aleksandr Beznosikov, Martin Takác, Samuel Horváth

CoRR, 2024

Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning.

Dmitry Bylinkin, Kirill Degtyarev, Aleksandr Beznosikov

CoRR, 2024

Gradient Clipping Improves AdaGrad when the Noise Is Heavy-Tailed.

Saveliy Chezhegov, Yaroslav Klyukin, Andrei Semenov, Aleksandr Beznosikov, Alexander V. Gasnikov, Samuel Horváth, Martin Takác, Eduard Gorbunov

CoRR, 2024

Local Methods with Adaptivity via Scaling.

Saveliy Chezhegov, Sergey Skorik, Nikolas Khachaturov, Danil Shalagin, Aram Avetisyan, Aleksandr Beznosikov, Martin Takác, Yaroslav Kholodov, Alexander V. Gasnikov

CoRR, 2024

Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning.

Andrei Semenov, Vladimir Ivanov, Aleksandr Beznosikov, Alexander V. Gasnikov

CoRR, 2024

Optimal Data Splitting in Distributed Optimization for Machine Learning.

Daniil Medyakov, Gleb Molodtsov, Aleksandr Beznosikov, Alexander V. Gasnikov

CoRR, 2024

Activations and Gradients Compression for Model-Parallel Training.

Mikhail Rudakov, Aleksandr Beznosikov, Yaroslav Kholodov, Alexander V. Gasnikov

CoRR, 2024

Extragradient Sliding for Composite Non-monotone Variational Inequalities.

Roman Emelyanov, Andrey Tikhomirov, Aleksandr Beznosikov, Alexander Gasnikov

Proceedings of the Optimization and Applications - 15th International Conference, 2024

Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features.

Aleksandr Beznosikov, David Dobre, Gauthier Gidel

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

Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting.

Aleksei Ustimenko, Aleksandr Beznosikov

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases.

Ruslan Nazykov, Aleksandr Shestakov, Vladimir Solodkin, Aleksandr Beznosikov, Gauthier Gidel, Alexander V. Gasnikov

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Non-smooth setting of stochastic decentralized convex optimization problem over time-varying Graphs.

Aleksandr V. Lobanov, Andrew Veprikov, Georgiy Konin, Aleksandr Beznosikov, Alexander V. Gasnikov, Dmitry Kovalev

Comput. Manag. Sci., December, 2023

On Biased Compression for Distributed Learning.

Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik, Mher Safaryan

J. Mach. Learn. Res., 2023

Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities.

Aleksandr Beznosikov, Alexander V. Gasnikov

CoRR, 2023

Real Acceleration of Communication Process in Distributed Algorithms with Compression.

Svetlana Tkachenko, Artem Andreev, Aleksandr Beznosikov, Alexander V. Gasnikov

Proceedings of the Optimization and Applications - 14th International Conference, 2023

Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities.

Aleksandr Beznosikov, Martin Takác, Alexander V. Gasnikov

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

First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities.

Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova, Alexander V. Gasnikov, Alexey Naumov, Eric Moulines

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

Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods.

Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou

Proceedings of the International Conference on Artificial Intelligence and Statistics, 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

SARAH-based Variance-reduced Algorithm for Stochastic Finite-sum Cocoercive Variational Inequalities.

Aleksandr Beznosikov, Alexander V. Gasnikov

CoRR, 2022

Smooth Monotone Stochastic Variational Inequalities and Saddle Point Problems - Survey.

Aleksandr Beznosikov, Boris T. Polyak, Eduard Gorbunov, Dmitry Kovalev, Alexander V. Gasnikov

CoRR, 2022

On Scaled Methods for Saddle Point Problems.

Aleksandr Beznosikov, Aibek Alanov, Dmitry Kovalev, Martin Takác, Alexander V. Gasnikov

CoRR, 2022

Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity.

Dmitry Kovalev, Aleksandr Beznosikov, Ekaterina Borodich, Alexander V. Gasnikov, Gesualdo Scutari

CoRR, 2022

Compression and Data Similarity: Combination of Two Techniques for Communication-Efficient Solving of Distributed Variational Inequalities.

Aleksandr Beznosikov, Alexander V. Gasnikov

Proceedings of the Optimization and Applications - 13th International Conference, 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

Optimal Gradient Sliding and its Application to Optimal Distributed Optimization Under Similarity.

Dmitry Kovalev, Aleksandr Beznosikov, Ekaterina Borodich, Alexander V. Gasnikov, Gesualdo Scutari

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

Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees.

Aleksandr Beznosikov, Peter Richtárik, Michael Diskin, Max Ryabinin, Alexander V. Gasnikov

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

Decentralized Local Stochastic Extra-Gradient for Variational Inequalities.

Aleksandr Beznosikov, Pavel E. Dvurechensky, Anastasia Koloskova, Valentin Samokhin, Sebastian U. Stich, Alexander V. Gasnikov

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

The power of first-order smooth optimization for black-box non-smooth problems.

Alexander V. Gasnikov, Anton Novitskii, Vasilii Novitskii, Farshed Abdukhakimov, Dmitry Kamzolov, Aleksandr Beznosikov, Martin Takác, Pavel E. Dvurechensky, Bin Gu

Proceedings of the International Conference on Machine Learning, 2022

2021

Distributed Saddle-Point Problems Under Similarity.

Aleksandr Beznosikov, Gesualdo Scutari, Alexander Rogozin, Alexander V. Gasnikov

CoRR, 2021

One-Point Gradient-Free Methods for Composite Optimization with Applications to Distributed Optimization.

Ivan Stepanov, Artyom Voronov, Aleksandr Beznosikov, Alexander V. Gasnikov

CoRR, 2021

Decentralized Personalized Federated Min-Max Problems.

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

CoRR, 2021

Decentralized Distributed Optimization for Saddle Point Problems.

Alexander Rogozin, Aleksandr Beznosikov, Darina Dvinskikh, Dmitry Kovalev, Pavel E. Dvurechensky, Alexander V. Gasnikov

CoRR, 2021

Near-Optimal Decentralized Algorithms for Saddle Point Problems over Time-Varying Networks.

Aleksandr Beznosikov, Alexander Rogozin, Dmitry Kovalev, Alexander V. Gasnikov

Proceedings of the Optimization and Applications - 12th International Conference, 2021

Distributed Saddle-Point Problems Under Data Similarity.

Aleksandr Beznosikov, Gesualdo Scutari, Alexander Rogozin, Alexander V. Gasnikov

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

One-Point Gradient-Free Methods for Smooth and Non-smooth Saddle-Point Problems.

Aleksandr Beznosikov, Vasilii Novitskii, Alexander V. Gasnikov

Proceedings of the Mathematical Optimization Theory and Operations Research, 2021

2020

Local SGD for Saddle-Point Problems.

Aleksandr Beznosikov, Valentin Samokhin, Alexander V. Gasnikov

CoRR, 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