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Publications

Stochastic Gradient Methods with Preconditioned Updates

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

June 2022

Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity

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

May 2022

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

Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou

February 2022

Optimal Algorithms for Decentralized Stochastic Variational Inequalities

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

February 2022

The Power of First-Order Smooth Optimization for Black-Box Non-Smooth Problems

Alexander Gasnikov, Anton Novitskii, Vasilii Novitskii, Farshed Abdukhakimov, Dmitry Kamzolov, Aleksandr Beznosikov, Martin Takáč, Pavel Dvurechensky, Bin Gu

Short talk at ICML 2022

January 2022

A Unified Analysis of Variational Inequality Methods: Variance Reduction, Sampling, Quantization and Coordinate Descent

Aleksandr Beznosikov, Alexander Gasnikov, Karina Zainulina, Alexander Maslovskiy, Dmitry Pasechnyuk

January 2022

Random-reshuffled SARAH does not need a full gradient computations

Aleksandr Beznosikov, Martin Takac

Poster at NeurIPS 2021 Workshop on Optimization for Machine Learning (virtual)

November 2021

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

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

October 2021

Distributed Saddle-Point Problems Under Similarity

Aleksandr Beznosikov, Gesualdo Scutari, Alexander Rogozin, Alexander Gasnikov

Poster at NeurIPS 2021 (virtual)

July 2021

Decentralized and Personalized Federated Learning

Abdurakhmon Sadiev, Ekaterina Borodich, Aleksandr Beznosikov, Darina Dvinskikh, Martin Takac, Alexander Gasnikov

Spotlight at NeurIPS 2021 Workshop on Optimization for Machine Learning (virtual)

July 2021

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

Aleksandr Beznosikov, Alexander Rogozin, Dmitry Kovalev, Alexander Gasnikov

OPTIMA 2021 (Petrovac, Montenegro), LNCS series

July 2021

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

Ivan Stepanov, Artyom Voronov, Aleksandr Beznosikov, Alexander Gasnikov

July 2021

Decentralized Local Stochastic Extra-Gradient for Variational Inequalities

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

June 2021

Decentralized Personalized Federated Min-Max Problems

Ekaterina Borodich, Aleksandr Beznosikov, Abdurakhmon Sadiev, Vadim Sushko, Nikolay Savelyev, Martin Takac, Alexander Gasnikov

Poster at NeurIPS 2020 Workshop on New Frontiers in Federated Learning:
Privacy, Fairness, Robustness, Personalization and Data Ownership (virtual)

June 2021

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

Aleksandr Beznosikov, Vasilii Novitskii and Alexander Gasnikov

MOTOR 2021 (Irkutsk, Russia), LNCS series

March 2021

Solving smooth min-min and min-max problems by mixed oracle algorithms

Egor Gladin, Abdurakhmon Sadiev, Alexander Gasnikov, Pavel Dvurechensky, Aleksandr Beznosikov, Mohammad Alkousa

MOTOR 2021 (Irkutsk, Russia), CCIS series

March 2021

Distributed Saddle-Point Problems: Lower Bounds, Optimal Algorithms and Robust Algorithms

Aleksandr Beznosikov, Valentin Samokhin and Alexander Gasnikov

February 2021

Decentralized Distributed Optimization for Saddle Point Problems

Alexander Rogozin, Aleksandr Beznosikov, Darina Dvinskikh, Dmitry Kovalev, Pavel Dvurechensky and Alexander Gasnikov

February 2021

Recent theoretical advances in decentralized distributed convex optimization

Eduard Gorbunov, Alexander Rogozin, Aleksandr Beznosikov, Darina Dvinskikh, Alexander Gasnikov

High Dimensional Optimization and Probability Journal

November 2020

Zeroth-Order Algorithms for Smooth Saddle-Point Problems

Abdurakhmon Sadiev, Aleksandr Beznosikov, Pavel Dvurechensky, Alexander Gasnikov

MOTOR 2021 (Irkutsk, Russia), CCIS series

September 2020

Linearly Convergent Gradient-Free Methods for Minimization of Symmetric Parabolic Approximation

Aleksandra Bazarova, Aleksandr Beznosikov and Alexander Gasnikov

September 2020

Gradient-Free Methods for Saddle-Point Problem

Aleksandr Beznosikov, Abdurakhmon Sadiev and Alexander Gasnikov

MOTOR 2020 (Novosibirsk, Russia), CCIS series

May 2020

On Biased Compression for Distributed Learning

Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik and Mher Safaryan

Oral talk at NeurIPS 2020 Workshop on Scalability, Privacy and Security in Federated Learning (virtual)

Febuary 2020

Derivative-Free Method For Decentralized Distributed Non-Smooth Optimization

Aleksandr Beznosikov, Eduard Gorbunov and Alexander Gasnikov

Poster at IFAC World Congress 2020 (Berlin, Germany), IFAC Papers Online

November 2019

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