Kovalev, Dmitry

26 publications

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

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

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

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

AISTATS 2022 An Optimal Algorithm for Strongly Convex Minimization Under Affine Constraints Adil Salim, Laurent Condat, Dmitry Kovalev, Peter Richtarik

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 Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox Abdurakhmon Sadiev, Dmitry Kovalev, Peter Richtarik

ICLR 2022 IntSGD: Adaptive Floatless Compression of Stochastic Gradients Konstantin Mishchenko, Bokun Wang, Dmitry Kovalev, Peter Richtárik

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

AISTATS 2021 A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtarik, Sebastian Stich

ICML 2021 ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks Dmitry Kovalev, Egor Shulgin, Peter Richtarik, Alexander V 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 2020 Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization Zhize Li, Dmitry Kovalev, Xun Qian, Peter Richtarik

ALT 2020 Don’t Jump Through Hoops and Remove Those Loops: SVRG and Katyusha Are Better Without the Outer Loop Dmitry Kovalev, Samuel Horváth, Peter Richtárik

ICML 2020 From Local SGD to Local Fixed-Point Methods for Federated Learning Grigory Malinovskiy, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtarik

NeurIPS 2020 Linearly Converging Error Compensated SGD Eduard Gorbunov, Dmitry Kovalev, Dmitry Makarenko, Peter Richtarik

NeurIPS 2020 Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization Dmitry Kovalev, Adil Salim, Peter Richtarik

AISTATS 2020 Revisiting Stochastic Extragradient Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtarik, Yura Malitsky

ICML 2020 Variance Reduced Coordinate Descent with Acceleration: New Method with a Surprising Application to Finite-Sum Problems Filip Hanzely, Dmitry Kovalev, Peter Richtarik

NeurIPS 2019 RSN: Randomized Subspace Newton Robert Gower, Dmitry Kovalev, Felix Lieder, Peter Richtarik

NeurIPS 2019 Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates Adil Salim, Dmitry Kovalev, Peter Richtarik

NeurIPS 2018 Stochastic Spectral and Conjugate Descent Methods Dmitry Kovalev, Peter Richtarik, Eduard Gorbunov, Elnur Gasanov