Aleksandr Beznosikov

90 papers A* 15A 3Journal 67Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problems. CoRR Dmitry Bylinkin, Sergey Skorik, Dmitriy Bystrov, Leonid Berezin, Aram Avetisyan, Aleksandr Beznosikov
2026	J	`jnl`	Accelerated Methods with Compression for Horizontal and Vertical Federated Learning. J. Optim. Theory Appl. Sergey Stanko, Timur Karimullin, Aleksandr Beznosikov, Alexander V. Gasnikov
2026	A*	`conf`	Bant: Byzantine Antidote via Trial Function and Trust Scores. AAAI Gleb Molodtsov, Daniil Medyakov, Sergey Skorik, Nikolas Khachaturov, Shahane Tigranyan, Vladimir Aletov, Aram Avetisyan, Martin Takác, Aleksandr Beznosikov
2026	J	`jnl`	Beyond SGD, Without SVD: Proximal Subspace Iteration LoRA with Diagonal Fractional K-FAC. CoRR Abdulla Jasem Almansoori, Maria Ivanova, Andrey Veprikov, Aleksandr Beznosikov, Samuel Horváth, Martin Takác
2026	J	`jnl`	Bridging KANs and Tabular Deep Learning: Feature Embeddings and Efficient Ensembling. IEEE Access Georgii Bulgakov, Danil Rudenko, Gleb Molodtsov, Aleksandr Beznosikov
2026	J	`jnl`	Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation. CoRR Aleksandr Beznosikov, Georgiy Kormakov, Alexander Grigorievskiy, Mikhail Rudakov, Ruslan Nazykov, Alexander Rogozin, Anton Vakhrushev, Andrey V. Savchenko, Martin Takác, Alexander V. Gasnikov
2026	J	`jnl`	Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation. J. Optim. Theory Appl. Aleksandr Beznosikov, Georgiy Kormakov, Alexander Grigorievskiy, Mikhail Rudakov, Ruslan Nazykov, Alexander Rogozin, Anton Vakhrushev, Andrey V. Savchenko, Martin Takác, Alexander V. Gasnikov
2026	J	`jnl`	Gradient-Free Approaches is a Key to an Efficient Interaction with Markovian Stochasticity. CoRR Boris Prokhorov, Semyon Chebykin, Alexander V. Gasnikov, Aleksandr Beznosikov
2026	A*	`conf`	Methods for Optimization Problems with Markovian Stochasticity and Non-Euclidean Geometry. AAAI Vladimir Solodkin, Andrey Veprikov, Alexander Chernyavskiy, Aleksandr Beznosikov
2026	J	`jnl`	Thinking Like a CHEMIST: Combined Heterogeneous Embedding Model Integrating Structure and Tokens. IEEE Access Nikolai Rekut, Alexey A. Orlov, Klea Ziu, Elizaveta Starykh, Martin Takác, Aleksandr Beznosikov
2026	J	`jnl`	Where Does Warm-Up Come From? Adaptive Scheduling for Norm-Constrained Optimizers. CoRR Artem Riabinin, Andrey Veprikov, Arman Bolatov, Martin Takác, Aleksandr Beznosikov
2026	J	`jnl`	Zero-Order Optimization for LLM Fine-Tuning via Learnable Direction Sampling. CoRR Valery Parfenov, Grigoriy Evseev, Andrey Veprikov, Nikolay Bushkov, Stanislav Moiseev, Aleksandr Beznosikov
2025	A*	`conf`	Accelerated Methods with Compressed Communications for Distributed Optimization Problems Under Data Similarity. AAAI Dmitry Bylinkin, Aleksandr Beznosikov
2025	J	`jnl`	Aligning Distributionally Robust Optimization with Practical Deep Learning Needs. CoRR Dmitrii Feoktistov, Igor Ignashin, Andrey Veprikov, Nikita Borovko, Alexander Bogdanov, Savelii Chezhegov, Aleksandr Beznosikov
2025	J	`jnl`	Broadening Discovery through Structural Models: Multimodal Combination of Local and Structural Properties for Predicting Chemical Features. CoRR Nikolai Rekut, Alexey A. Orlov, Klea Ziu, Elizaveta Starykh, Martin Takác, Aleksandr Beznosikov
2025	A*	`conf`	Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed. ICML Savelii Chezhegov, Yaroslav Klyukin, Andrei Semenov, Aleksandr Beznosikov, Alexander V. Gasnikov, Samuel Horváth, Martin Takác, Eduard Gorbunov
2025	J	`jnl`	Communication-Efficient Federated Learning with Adaptive Number of Participants. CoRR Sergey Skorik, Vladislav Dorofeev, Gleb Molodtsov, Aram Avetisyan, Dmitry Bylinkin, Daniil Medyakov, Aleksandr Beznosikov
2025	J	`jnl`	Convergence of Clipped-SGD for Convex (L CoRR Saveliy Chezhegov, Aleksandr Beznosikov, Samuel Horváth, Eduard Gorbunov
2025	J	`jnl`	Decentralized saddle point problems via non-Euclidean mirror prox. Optim. Methods Softw. Alexander Rogozin, Aleksandr Beznosikov, Darina Dvinskikh, Dmitry Kovalev, Pavel E. Dvurechensky, Alexander V. Gasnikov
2025	J	`jnl`	Distributed saddle point problems: lower bounds, near-optimal and robust algorithms. Optim. Methods Softw. Aleksandr Beznosikov, Valentin Samokhin, Alexander V. Gasnikov
2025	J	`jnl`	Enhancing Stability of Physics-Informed Neural Network Training Through Saddle-Point Reformulation. CoRR Dmitry Bylinkin, Mikhail Aleksandrov, Saveliy Chezhegov, Aleksandr Beznosikov
2025	A*	`conf`	FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training. ICML Philip Zmushko, Aleksandr Beznosikov, Martin Takác, Samuel Horváth
2025	J	`jnl`	Faster Than SVD, Smarter Than SGD: The OPLoRA Alternating Update. CoRR Abdulla Jasem Almansoori, Maria Ivanova, Andrey Veprikov, Aleksandr Beznosikov, Samuel Horváth, Martin Takác
2025	J	`jnl`	Leveraging Coordinate Momentum in SignSGD and Muon: Memory-Optimized Zero-Order. CoRR Egor Petrov, Grigoriy Evseev, Aleksey Antonov, Andrey Veprikov, Pavel Plyusnin, Nikolay Bushkov, Stanislav Moiseev, Aleksandr Beznosikov
2025	J	`jnl`	One-point feedback for composite optimization with applications to distributed and federated learning. Optim. Methods Softw. Aleksandr Beznosikov, Ivan Stepanov, Artyom Voronov, Alexander V. Gasnikov
2025	J	`jnl`	Preconditioned Norms: A Unified Framework for Steepest Descent, Quasi-Newton and Adaptive Methods. CoRR Andrey Veprikov, Arman Bolatov, Samuel Horváth, Aleksandr Beznosikov, Martin Takác, Slavomír Hanzely
2025	J	`jnl`	Shuffling Heuristic in Variational Inequalities: Establishing New Convergence Guarantees. CoRR Daniil Medyakov, Gleb Molodtsov, Grigoriy Evseev, Egor Petrov, Aleksandr Beznosikov
2025	J	`jnl`	Sign Operator for Coping with Heavy-Tailed Noise: High Probability Convergence Bounds with Extensions to Distributed Optimization and Comparison Oracle. CoRR Nikita Kornilov, Philip Zmushko, Andrei Semenov, Alexander V. Gasnikov, Aleksandr Beznosikov
2025	J	`jnl`	Sign-SGD is the Golden Gate between Multi-Node to Single-Node Learning: Significant Boost via Parameter-Free Optimization. CoRR Daniil Medyakov, Sergey Stanko, Gleb Molodtsov, Philip Zmushko, Grigoriy Evseev, Egor Petrov, Aleksandr Beznosikov
2025	J	`jnl`	Trial and Trust: Addressing Byzantine Attacks with Comprehensive Defense Strategy. CoRR Gleb Molodtsov, Daniil Medyakov, Sergey Skorik, Nikolas Khachaturov, Shahane Tigranyan, Vladimir Aletov, Aram Avetisyan, Martin Takác, Aleksandr Beznosikov
2025	J	`jnl`	Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling. CoRR Daniil Medyakov, Gleb Molodtsov, Saveliy Chezhegov, Alexey Rebrikov, Aleksandr Beznosikov
2025	J	`jnl`	WeightLoRA: Keep Only Necessary Adapters. CoRR Andrey Veprikov, Vladimir Solodkin, Alexander Zyl, Andrey V. Savchenko, Aleksandr Beznosikov
2025	A	`conf`	When Extragradient Meets PAGE: Bridging Two Giants to Boost Variational Inequalities. UAI Gleb Molodtsov, Valery Parfenov, Egor Petrov, Grigoriy Evseev, Daniil Medyakov, Aleksandr Beznosikov
2024	J	`jnl`	Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity. CoRR Dmitry Bylinkin, Aleksandr Beznosikov
2024	J	`jnl`	Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning. CoRR Dmitry Bylinkin, Kirill Degtyarev, Aleksandr Beznosikov
2024	J	`jnl`	Activations and Gradients Compression for Model-Parallel Training. CoRR Mikhail Rudakov, Aleksandr Beznosikov, Yaroslav Kholodov, Alexander V. Gasnikov
2024	J	`jnl`	Decentralized optimization over slowly time-varying graphs: algorithms and lower bounds. Comput. Manag. Sci. Dmitry Metelev, Aleksandr Beznosikov, Alexander Rogozin, Alexander V. Gasnikov, Anton V. Proskurnikov
2024	—	`conf`	Extragradient Sliding for Composite Non-monotone Variational Inequalities. OPTIMA Roman Emelyanov, Andrey Tikhomirov, Aleksandr Beznosikov, Alexander V. Gasnikov
2024	J	`jnl`	FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training. CoRR Philip Zmushko, Aleksandr Beznosikov, Martin Takác, Samuel Horváth
2024	J	`jnl`	Gradient Clipping Improves AdaGrad when the Noise Is Heavy-Tailed. CoRR Saveliy Chezhegov, Yaroslav Klyukin, Andrei Semenov, Aleksandr Beznosikov, Alexander V. Gasnikov, Samuel Horváth, Martin Takác, Eduard Gorbunov
2024	A*	`conf`	Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting. ICLR Aleksei Ustimenko, Aleksandr Beznosikov
2024	J	`jnl`	Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learning. CoRR Andrei Semenov, Philip Zmushko, Alexander Pichugin, Aleksandr Beznosikov
2024	J	`jnl`	Label Privacy in Split Learning for Large Models with Parameter-Efficient Training. CoRR Philip Zmushko, Marat Mansurov, Ruslan Svirschevski, Denis Kuznedelev, Max Ryabinin, Aleksandr Beznosikov
2024	J	`jnl`	Local Methods with Adaptivity via Scaling. CoRR Saveliy Chezhegov, Sergey Skorik, Nikolas Khachaturov, Danil Shalagin, Aram Avetisyan, Aleksandr Beznosikov, Martin Takác, Yaroslav Kholodov, Alexander V. Gasnikov
2024	J	`jnl`	Optimal Data Splitting in Distributed Optimization for Machine Learning. CoRR Daniil Medyakov, Gleb Molodtsov, Aleksandr Beznosikov, Alexander V. Gasnikov
2024	J	`jnl`	Preconditioning meets biased compression for efficient distributed optimization. Comput. Manag. Sci. Vitali Pirau, Aleksandr Beznosikov, Martin Takác, Vladislav Matyukhin, Alexander V. Gasnikov
2024	J	`jnl`	Random-reshuffled SARAH does not need full gradient computations. Optim. Lett. Aleksandr Beznosikov, Martin Takác
2024	A*	`conf`	Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features. ICML Aleksandr Beznosikov, David Dobre, Gauthier Gidel
2024	J	`jnl`	Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning. CoRR Andrei Semenov, Vladimir Ivanov, Aleksandr Beznosikov, Alexander V. Gasnikov
2024	A	`conf`	Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases. AISTATS Ruslan Nazykov, Aleksandr Shestakov, Vladimir Solodkin, Aleksandr Beznosikov, Gauthier Gidel, Alexander V. Gasnikov
2024	J	`jnl`	Stochastic Gradient Methods with Preconditioned Updates. J. Optim. Theory Appl. Abdurakhmon Sadiev, Aleksandr Beznosikov, Abdulla Jasem Almansoori, Dmitry Kamzolov, Rachael Tappenden, Martin Takác
2023	A*	`conf`	First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities. NeurIPS Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova, Alexander V. Gasnikov, Alexey Naumov, Eric Moulines
2023	J	`jnl`	First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities. CoRR Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova, Alexander V. Gasnikov, Alexey Naumov, Eric Moulines
2023	J	`jnl`	Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting. CoRR Aleksei Ustimenko, Aleksandr Beznosikov
2023	J	`jnl`	Non-smooth setting of stochastic decentralized convex optimization problem over time-varying Graphs. Comput. Manag. Sci. Aleksandr V. Lobanov, Andrew Veprikov, Georgiy Konin, Aleksandr Beznosikov, Alexander V. Gasnikov, Dmitry Kovalev
2023	J	`jnl`	On Biased Compression for Distributed Learning. J. Mach. Learn. Res. Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik, Mher Safaryan
2023	—	`conf`	Real Acceleration of Communication Process in Distributed Algorithms with Compression. OPTIMA Svetlana Tkachenko, Artem Andreev, Aleksandr Beznosikov, Alexander V. Gasnikov
2023	J	`jnl`	Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features. CoRR Aleksandr Beznosikov, David Dobre, Gauthier Gidel
2023	A*	`conf`	Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities. NeurIPS Aleksandr Beznosikov, Martin Takác, Alexander V. Gasnikov
2023	J	`jnl`	Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities. CoRR Aleksandr Beznosikov, Alexander V. Gasnikov
2023	A	`conf`	Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods. AISTATS Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou
2022	—	`conf`	Compression and Data Similarity: Combination of Two Techniques for Communication-Efficient Solving of Distributed Variational Inequalities. OPTIMA Aleksandr Beznosikov, Alexander V. Gasnikov
2022	J	`jnl`	Compression and Data Similarity: Combination of Two Techniques for Communication-Efficient Solving of Distributed Variational Inequalities. CoRR Aleksandr Beznosikov, Alexander V. Gasnikov
2022	A*	`conf`	Decentralized Local Stochastic Extra-Gradient for Variational Inequalities. NeurIPS Aleksandr Beznosikov, Pavel E. Dvurechensky, Anastasia Koloskova, Valentin Samokhin, Sebastian U. Stich, Alexander V. Gasnikov
2022	J	`jnl`	Decentralized personalized federated learning: Lower bounds and optimal algorithm for all personalization modes. EURO J. Comput. Optim. Abdurakhmon Sadiev, Ekaterina Borodich, Aleksandr Beznosikov, Darina Dvinskikh, Saveliy Chezhegov, Rachael Tappenden, Martin Takác, Alexander V. Gasnikov
2022	A*	`conf`	Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees. NeurIPS Aleksandr Beznosikov, Peter Richtárik, Michael Diskin, Max Ryabinin, Alexander V. Gasnikov
2022	J	`jnl`	On Scaled Methods for Saddle Point Problems. CoRR Aleksandr Beznosikov, Aibek Alanov, Dmitry Kovalev, Martin Takác, Alexander V. Gasnikov
2022	A*	`conf`	Optimal Algorithms for Decentralized Stochastic Variational Inequalities. NeurIPS Dmitry Kovalev, Aleksandr Beznosikov, Abdurakhmon Sadiev, Michael Persiianov, Peter Richtárik, Alexander V. Gasnikov
2022	J	`jnl`	Optimal Algorithms for Decentralized Stochastic Variational Inequalities. CoRR Dmitry Kovalev, Aleksandr Beznosikov, Abdurakhmon Sadiev, Michael Persiianov, Peter Richtárik, Alexander V. Gasnikov
2022	J	`jnl`	Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity. CoRR Dmitry Kovalev, Aleksandr Beznosikov, Ekaterina Borodich, Alexander V. Gasnikov, Gesualdo Scutari
2022	A*	`conf`	Optimal Gradient Sliding and its Application to Optimal Distributed Optimization Under Similarity. NeurIPS Dmitry Kovalev, Aleksandr Beznosikov, Ekaterina Borodich, Alexander V. Gasnikov, Gesualdo Scutari
2022	J	`jnl`	SARAH-based Variance-reduced Algorithm for Stochastic Finite-sum Cocoercive Variational Inequalities. CoRR Aleksandr Beznosikov, Alexander V. Gasnikov
2022	J	`jnl`	Smooth Monotone Stochastic Variational Inequalities and Saddle Point Problems - Survey. CoRR Aleksandr Beznosikov, Boris T. Polyak, Eduard Gorbunov, Dmitry Kovalev, Alexander V. Gasnikov
2022	J	`jnl`	Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods. CoRR Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou
2022	J	`jnl`	Stochastic Gradient Methods with Preconditioned Updates. CoRR Abdurakhmon Sadiev, Aleksandr Beznosikov, Abdulla Jasem Almansoori, Dmitry Kamzolov, Rachael Tappenden, Martin Takác
2022	A*	`conf`	The power of first-order smooth optimization for black-box non-smooth problems. ICML Alexander V. Gasnikov, Anton Novitskii, Vasilii Novitskii, Farshed Abdukhakimov, Dmitry Kamzolov, Aleksandr Beznosikov, Martin Takác, Pavel E. Dvurechensky, Bin Gu
2021	J	`jnl`	Decentralized Distributed Optimization for Saddle Point Problems. CoRR Alexander Rogozin, Aleksandr Beznosikov, Darina Dvinskikh, Dmitry Kovalev, Pavel E. Dvurechensky, Alexander V. Gasnikov
2021	J	`jnl`	Decentralized Local Stochastic Extra-Gradient for Variational Inequalities. CoRR Aleksandr Beznosikov, Pavel E. Dvurechensky, Anastasia Koloskova, Valentin Samokhin, Sebastian U. Stich, Alexander V. Gasnikov
2021	J	`jnl`	Decentralized Personalized Federated Min-Max Problems. CoRR Aleksandr Beznosikov, Vadim Sushko, Abdurakhmon Sadiev, Alexander V. Gasnikov
2021	J	`jnl`	Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees. CoRR Aleksandr Beznosikov, Peter Richtárik, Michael Diskin, Max Ryabinin, Alexander V. Gasnikov
2021	A*	`conf`	Distributed Saddle-Point Problems Under Data Similarity. NeurIPS Aleksandr Beznosikov, Gesualdo Scutari, Alexander Rogozin, Alexander V. Gasnikov
2021	J	`jnl`	Distributed Saddle-Point Problems Under Similarity. CoRR Aleksandr Beznosikov, Gesualdo Scutari, Alexander Rogozin, Alexander V. Gasnikov
2021	—	`conf`	Near-Optimal Decentralized Algorithms for Saddle Point Problems over Time-Varying Networks. OPTIMA Aleksandr Beznosikov, Alexander Rogozin, Dmitry Kovalev, Alexander V. Gasnikov
2021	J	`jnl`	One-Point Gradient-Free Methods for Composite Optimization with Applications to Distributed Optimization. CoRR Ivan Stepanov, Artyom Voronov, Aleksandr Beznosikov, Alexander V. Gasnikov
2021	—	`conf`	One-Point Gradient-Free Methods for Smooth and Non-smooth Saddle-Point Problems. MOTOR Aleksandr Beznosikov, Vasilii Novitskii, Alexander V. Gasnikov
2021	J	`jnl`	Random-reshuffled SARAH does not need a full gradient computations. CoRR Aleksandr Beznosikov, Martin Takác
2020	J	`jnl`	Gradient-Free Methods for Saddle-Point Problem. CoRR Aleksandr Beznosikov, Abdurakhmon Sadiev, Alexander V. Gasnikov
2020	J	`jnl`	Local SGD for Saddle-Point Problems. CoRR Aleksandr Beznosikov, Valentin Samokhin, Alexander V. Gasnikov
2020	J	`jnl`	On Biased Compression for Distributed Learning. CoRR Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik, Mher Safaryan
2020	J	`jnl`	Zeroth-Order Algorithms for Smooth Saddle-Point Problems. CoRR Abdurakhmon Sadiev, Aleksandr Beznosikov, Pavel E. Dvurechensky, Alexander V. Gasnikov