Gasnikov, Alexander

44 publications

UAI 2025 An Optimal Algorithm for Strongly Convex Min-Min Optimization Dmitry Kovalev, Alexander Gasnikov, Grigory Malinovsky

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

ICLR 2025 Decentralized Optimization with Coupled Constraints Demyan Yarmoshik, Alexander Rogozin, Nikita Kiselev, Daniil Dorin, Alexander Gasnikov, Dmitry Kovalev

ICLR 2025 OPTAMI: Global Superlinear Convergence of High-Order Methods Dmitry Kamzolov, Artem Agafonov, Dmitry Pasechnyuk, Alexander Gasnikov, Martin Takáč

ICML 2025 On Linear Convergence in Smooth Convex-Concave Bilinearly-Coupled Saddle-Point Optimization: Lower Bounds and Optimal Algorithms Ekaterina Borodich, Alexander Gasnikov, Dmitry Kovalev

NeurIPS 2024 Acceleration Exists! Optimization Problems When Oracle Can Only Compare Objective Function Values Aleksandr Lobanov, Alexander Gasnikov, Andrei Krasnov

NeurIPS 2024 Achieving Linear Convergence with Parameter-Free Algorithms in Decentralized Optimization Ilya Kuruzov, Gesualdo Scutari, Alexander Gasnikov

ICLR 2024 Advancing the Lower Bounds: An Accelerated, Stochastic, Second-Order Method with Optimal Adaptation to Inexactness Artem Agafonov, Dmitry Kamzolov, Alexander Gasnikov, Ali Kavis, Kimon Antonakopoulos, Volkan Cevher, Martin Takáč

AISTATS 2024 Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems Nikita Puchkin, Eduard Gorbunov, Nickolay Kutuzov, Alexander Gasnikov

NeurIPS 2024 Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations Artem Agafonov, Petr Ostroukhov, Roman Mozhaev, Konstantin Yakovlev, Eduard Gorbunov, Martin Takáč, Alexander Gasnikov, Dmitry Kamzolov

ICML 2024 High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise Eduard Gorbunov, Abdurakhmon Sadiev, Marina Danilova, Samuel Horváth, Gauthier Gidel, Pavel Dvurechensky, Alexander Gasnikov, Peter Richtárik

NeurIPS 2024 Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks Dmitry Kovalev, Ekaterina Borodich, Alexander Gasnikov, Dmitrii Feoktistov

NeurIPS 2024 Optimal Flow Matching: Learning Straight Trajectories in Just One Step Nikita Kornilov, Petr Mokrov, Alexander Gasnikov, Alexander Korotin

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 Accelerated Zeroth-Order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance Nikita Kornilov, Ohad Shamir, Aleksandr Lobanov, Darina Dvinskikh, Alexander Gasnikov, Innokentiy Shibaev, Eduard Gorbunov, Samuel Horváth

AISTATS 2023 Algorithm for Constrained Markov Decision Process with Linear Convergence Egor Gladin, Maksim Lavrik-Karmazin, Karina Zainullina, Varvara Rudenko, Alexander Gasnikov, Martin Takac

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

ICML 2023 High-Probability Bounds for Stochastic Optimization and Variational Inequalities: The Case of Unbounded Variance Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horváth, Gauthier Gidel, Pavel Dvurechensky, Alexander Gasnikov, Peter Richtárik

ICML 2023 Is Consensus Acceleration Possible in Decentralized Optimization over Slowly Time-Varying Networks? Dmitry Metelev, Alexander Rogozin, Dmitry Kovalev, Alexander Gasnikov

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

AISTATS 2022 Acceleration in Distributed Optimization Under Similarity Ye Tian, Gesualdo Scutari, Tianyu Cao, Alexander Gasnikov

AISTATS 2022 Primal-Dual Stochastic Mirror Descent for MDPs Daniil Tiapkin, Alexander Gasnikov

NeurIPS 2022 A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate Slavomír Hanzely, Dmitry Kamzolov, Dmitry Pasechnyuk, Alexander Gasnikov, Peter Richtarik, Martin Takac

NeurIPS 2022 Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling Dmitry Kovalev, Alexander Gasnikov, Peter Richtarik

NeurIPS 2022 Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise Eduard Gorbunov, Marina Danilova, David Dobre, Pavel Dvurechenskii, Alexander Gasnikov, Gauthier Gidel

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

NeurIPS 2022 The First Optimal Acceleration of High-Order Methods in Smooth Convex Optimization Dmitry Kovalev, Alexander Gasnikov

NeurIPS 2022 The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimization Dmitry Kovalev, Alexander Gasnikov

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

ICML 2021 ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks Dmitry Kovalev, Egor Shulgin, Peter Richtarik, Alexander V Rogozin, Alexander Gasnikov

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

NeurIPS 2021 Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization over Time-Varying Networks Dmitry Kovalev, Elnur Gasanov, Alexander Gasnikov, Peter Richtarik

ICML 2021 Newton Method over Networks Is Fast up to the Statistical Precision Amir Daneshmand, Gesualdo Scutari, Pavel Dvurechensky, Alexander Gasnikov

ICML 2021 On a Combination of Alternating Minimization and Nesterov’s Momentum Sergey Guminov, Pavel Dvurechensky, Nazarii Tupitsa, Alexander Gasnikov

NeurIPS 2020 Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping Eduard Gorbunov, Marina Danilova, Alexander Gasnikov

COLT 2019 Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-Th Derivatives Alexander Gasnikov, Pavel Dvurechensky, Eduard Gorbunov, Evgeniya Vorontsova, Daniil Selikhanovych, César A. Uribe, Bo Jiang, Haoyue Wang, Shuzhong Zhang, Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford

ICML 2019 On the Complexity of Approximating Wasserstein Barycenters Alexey Kroshnin, Nazarii Tupitsa, Darina Dvinskikh, Pavel Dvurechensky, Alexander Gasnikov, Cesar Uribe

COLT 2019 Optimal Tensor Methods in Smooth Convex and Uniformly ConvexOptimization Alexander Gasnikov, Pavel Dvurechensky, Eduard Gorbunov, Evgeniya Vorontsova, Daniil Selikhanovych, César A. Uribe

ICML 2018 Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better than by Sinkhorn’s Algorithm Pavel Dvurechensky, Alexander Gasnikov, Alexey Kroshnin

NeurIPS 2018 Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters Pavel Dvurechenskii, Darina Dvinskikh, Alexander Gasnikov, Cesar Uribe, Angelia Nedich

NeurIPS 2016 Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods Lev Bogolubsky, Pavel Dvurechenskii, Alexander Gasnikov, Gleb Gusev, Yurii Nesterov, Andrei M Raigorodskii, Aleksey Tikhonov, Maksim Zhukovskii