Beznosikov, Aleksandr

21 publications

ICLR 2026 Enhancing Stability of Physics-Informed Neural Network Training Through Saddle-Point Reformulation Dmitry Bylinkin, Mikhail Aleksandrov, Savelii Chezhegov, Aleksandr Beznosikov

ICLR 2026 Sign-SGD via Parameter-Free Optimization Daniil Medyakov, Stanko Sergey, Gleb Molodtsov, Philip Zmushko, Evseev Grigoriy, Egor Petrov, Aleksandr Beznosikov

ICLR 2026 Unlocking the Potential of Weighting Methods in Federated Learning Through Communication Compression Valery Parfenov, Nail Bashirov, Daniil Medyakov, Dmitry Bylinkin, Aleksandr Beznosikov

AAAI 2025 Accelerated Methods with Compressed Communications for Distributed Optimization Problems Under Data Similarity Dmitry Bylinkin, Aleksandr Beznosikov

ICML 2025 Clipping Improves Adam-Norm and AdaGrad-Norm When the Noise Is Heavy-Tailed Savelii Chezhegov, Klyukin Yaroslav, Andrei Semenov, Aleksandr Beznosikov, Alexander Gasnikov, Samuel Horváth, Martin Takáč, Eduard Gorbunov

ICML 2025 FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training Philip Zmushko, Aleksandr Beznosikov, Martin Takáč, Samuel Horváth

UAI 2025 When Extragradient Meets PAGE: Bridging Two Giants to Boost Variational Inequalities Gleb Molodtsov, Valery Parfenov, Egor Petrov, Evseev Grigoriy, Daniil Medyakov, Aleksandr Beznosikov

ICLR 2024 Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting Aleksei Ustimenko, Aleksandr Beznosikov

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

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

NeurIPS 2023 First Order Methods with Markovian Noise: From Acceleration to Variational Inequalities Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova, Alexander Gasnikov, Alexey Naumov, Eric Moulines

JMLR 2023 On Biased Compression for Distributed Learning Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik, Mher Safaryan

NeurIPS 2023 Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities Aleksandr Beznosikov, Martin Takac, Alexander Gasnikov

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

NeurIPS 2022 Decentralized Local Stochastic Extra-Gradient for Variational Inequalities Aleksandr Beznosikov, Pavel Dvurechenskii, Anastasiia Koloskova, Valentin Samokhin, Sebastian U Stich, Alexander Gasnikov

NeurIPS 2022 Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees Aleksandr Beznosikov, Peter Richtarik, Michael Diskin, Max Ryabinin, Alexander Gasnikov

NeurIPS 2022 Optimal Algorithms for Decentralized Stochastic Variational Inequalities Dmitry Kovalev, Aleksandr Beznosikov, Abdurakhmon Sadiev, Michael Persiianov, Peter Richtarik, Alexander Gasnikov

NeurIPS 2022 Optimal Gradient Sliding and Its Application to Optimal Distributed Optimization Under Similarity Dmitry Kovalev, Aleksandr Beznosikov, Ekaterina Borodich, Alexander Gasnikov, Gesualdo Scutari

NeurIPSW 2022 Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou

ICML 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 Takac, Pavel Dvurechensky, Bin Gu

NeurIPS 2021 Distributed Saddle-Point Problems Under Data Similarity Aleksandr Beznosikov, Gesualdo Scutari, Alexander Rogozin, Alexander Gasnikov