Gradient-Free Methods for Saddle-Point Problem. - Aleksandr Beznosikov; Abdurakhmon Sadiev; 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)

Local SGD for Saddle-Point Problems. - Aleksandr Beznosikov; Valentin Samokhin; Alexander V. Gasnikov (CoRR, 2020)

One-Point Gradient-Free Methods for Smooth and Non-smooth Saddle-Point Problems. - Aleksandr Beznosikov; Vasilii Novitskii; Alexander V. Gasnikov (MOTOR, 2021)

Distributed Saddle-Point Problems Under Data Similarity. - Aleksandr Beznosikov; Gesualdo Scutari; Alexander Rogozin; Alexander V. Gasnikov (NeurIPS, 2021)

Near-Optimal Decentralized Algorithms for Saddle Point Problems over Time-Varying Networks. - Aleksandr Beznosikov; Alexander Rogozin; Dmitry Kovalev; Alexander V. Gasnikov (OPTIMA, 2021)

Decentralized Distributed Optimization for Saddle Point Problems. - Alexander Rogozin; Aleksandr Beznosikov; Darina Dvinskikh; Dmitry Kovalev; Pavel E. Dvurechensky; Alexander V. Gasnikov (CoRR, 2021)

Decentralized Personalized Federated Min-Max Problems. - Aleksandr Beznosikov; Vadim Sushko; Abdurakhmon Sadiev; 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)

Distributed Saddle-Point Problems Under Similarity. - Aleksandr Beznosikov; Gesualdo Scutari; Alexander Rogozin; Alexander V. Gasnikov (CoRR, 2021)

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 001; Pavel E. Dvurechensky; Bin Gu 001 (ICML, 2022)

Decentralized Local Stochastic Extra-Gradient for Variational Inequalities. - Aleksandr Beznosikov; Pavel E. Dvurechensky; Anastasia Koloskova; Valentin Samokhin; Sebastian U. Stich; Alexander V. Gasnikov (NeurIPS, 2022)

Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees. - Aleksandr Beznosikov; Peter Richtárik; Michael Diskin; Max Ryabinin; Alexander V. Gasnikov (NeurIPS, 2022)

Optimal Gradient Sliding and its Application to Optimal Distributed Optimization Under Similarity. - Dmitry Kovalev; Aleksandr Beznosikov; Ekaterina Borodich; Alexander V. Gasnikov; Gesualdo Scutari (NeurIPS, 2022)

Optimal Algorithms for Decentralized Stochastic Variational Inequalities. - Dmitry Kovalev; Aleksandr Beznosikov; Abdurakhmon Sadiev; Michael Persiianov; Peter Richtárik; Alexander V. Gasnikov (NeurIPS, 2022)

Compression and Data Similarity: Combination of Two Techniques for Communication-Efficient Solving of Distributed Variational Inequalities. - Aleksandr Beznosikov; Alexander V. Gasnikov (OPTIMA, 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)

On Scaled Methods for Saddle Point Problems. - Aleksandr Beznosikov; Aibek Alanov; Dmitry Kovalev; Martin Takác 001; 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)

SARAH-based Variance-reduced Algorithm for Stochastic Finite-sum Cocoercive Variational Inequalities. - Aleksandr Beznosikov; Alexander V. Gasnikov (CoRR, 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 001; Alexander V. Gasnikov (EURO J. Comput. Optim., 2022)

Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods. - Aleksandr Beznosikov; Eduard Gorbunov; Hugo Berard; Nicolas Loizou (AISTATS, 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 (NeurIPS, 2023)

Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities. - Aleksandr Beznosikov; Martin Takác 001; Alexander V. Gasnikov (NeurIPS, 2023)

Real Acceleration of Communication Process in Distributed Algorithms with Compression. - Svetlana Tkachenko; Artem Andreev; Aleksandr Beznosikov; Alexander V. Gasnikov (OPTIMA, 2023)

Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities. - Aleksandr Beznosikov; Alexander V. Gasnikov (CoRR, 2023)

On Biased Compression for Distributed Learning. - Aleksandr Beznosikov; Samuel Horváth; Peter Richtárik; Mher Safaryan (J. Mach. Learn. Res., 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., 2023)

Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases. - Ruslan Nazykov; Aleksandr Shestakov; Vladimir Solodkin; Aleksandr Beznosikov; Gauthier Gidel; Alexander V. Gasnikov (AISTATS, 2024)

Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting. - Aleksei Ustimenko; Aleksandr Beznosikov (ICLR, 2024)

Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features. - Aleksandr Beznosikov; David Dobre; Gauthier Gidel (ICML, 2024)

Extragradient Sliding for Composite Non-monotone Variational Inequalities. - Roman Emelyanov; Andrey Tikhomirov; Aleksandr Beznosikov; Alexander Gasnikov (OPTIMA, 2024)

Activations and Gradients Compression for Model-Parallel Training. - Mikhail Rudakov; Aleksandr Beznosikov; Yaroslav Kholodov; 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)

Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning. - Andrei Semenov; Vladimir Ivanov; Aleksandr Beznosikov; Alexander V. Gasnikov (CoRR, 2024)

Local Methods with Adaptivity via Scaling. - Saveliy Chezhegov; Sergey Skorik; Nikolas Khachaturov; Danil Shalagin; Aram Avetisyan; Aleksandr Beznosikov; Martin Takác 001; Yaroslav Kholodov; Alexander V. Gasnikov (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 001; Eduard Gorbunov (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)

FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training. - Philip Zmushko; Aleksandr Beznosikov; Martin Takác 001; Samuel Horváth (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)

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)

Random-reshuffled SARAH does not need full gradient computations. - Aleksandr Beznosikov; Martin Takác 001 (Optim. Lett., 2024)

Stochastic Gradient Methods with Preconditioned Updates. - Abdurakhmon Sadiev; Aleksandr Beznosikov; Abdulla Jasem Almansoori; Dmitry Kamzolov; Rachael Tappenden; Martin Takác 001 (J. Optim. Theory Appl., 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., 2024)

Preconditioning meets biased compression for efficient distributed optimization. - Vitali Pirau; Aleksandr Beznosikov; Martin Takác 001; Vladislav Matyukhin; Alexander V. Gasnikov (Comput. Manag. Sci., 2024)

Accelerated Methods with Compressed Communications for Distributed Optimization Problems Under Data Similarity. - Dmitry Bylinkin; Aleksandr Beznosikov (AAAI, 2025)

When Extragradient Meets PAGE: Bridging Two Giants to Boost Variational Inequalities. - Gleb Molodtsov; Valery Parfenov; Egor Petrov; Grigoriy Evseev; Daniil Medyakov; Aleksandr Beznosikov (UAI, 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, 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, 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 001; Aleksandr Beznosikov (CoRR, 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 001; Aleksandr Beznosikov (CoRR, 2025)

Convergence of Clipped-SGD for Convex (L0,L1)-Smooth Optimization with Heavy-Tailed Noise. - Saveliy Chezhegov; Aleksandr Beznosikov; Samuel Horváth; Eduard Gorbunov (CoRR, 2025)

WeightLoRA: Keep Only Necessary Adapters. - Andrey Veprikov; Vladimir Solodkin; Alexander Zyl; Andrey Savchenko; Aleksandr Beznosikov (CoRR, 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, 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, 2025)

Enhancing Stability of Physics-Informed Neural Network Training Through Saddle-Point Reformulation. - Dmitry Bylinkin; Mikhail Aleksandrov; Saveliy Chezhegov; Aleksandr Beznosikov (CoRR, 2025)