← Authors

Dmitry Kovalev

26 papers · 2018–2025 · 6 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (7) 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (27) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🐝 Cross-Pollinator (3) 🌍 Conference Polyglot (6) 🀝 Dynamic Duo (18) πŸ”¬ Deep Specialist (14) 🧬 Topic Evolution πŸ† Keyword Champion (3) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (98) ⚑ Prolific Year (8) πŸ”₯ Unstoppable (8) πŸ’Ž Century Club (26) ❓ The Questioner

Conferences

NIPS (13) ICML (6) AISTATS (3) ICLR (2) ALT (1) UAI (1)

Top co-authors

Peter Richtarik (18) Alexander Gasnikov (12) Ekaterina Borodich (3) Adil SALIM (3) Elnur Gasanov (3) Alexander Rogozin (2) Egor Shulgin (2) Eduard Gorbunov (2) Aleksandr Beznosikov (2) Abdurakhmon Sadiev (2)

Keywords

convex optimization (10) decentralized optimization (7) communication complexity (6) distributed learning (6) optimal algorithm (5) distributed optimization (5) variance reduction (5) saddle-point problems (3) stochastic optimization (3) linear convergence (3) oracle complexity (3) time-varying network (3) federated learning (3) communication compression (2) primal-dual algorithm (2) variational inequality (2) network optimization (2) gradient descent (2) coordinate descent (2) strong convexity (2)

Papers

An Optimal Algorithm for Strongly Convex Min-Min Optimization UAI 2025
Decentralized Optimization with Coupled Constraints ICLR 2025
On Linear Convergence in Smooth Convex-Concave Bilinearly-Coupled Saddle-Point Optimization: Lower Bounds and Optimal Algorithms ICML 2025
Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks NIPS 2024
Is Consensus Acceleration Possible in Decentralized Optimization over Slowly Time-Varying Networks? ICML 2023
Optimal Algorithms for Decentralized Stochastic Variational Inequalities NIPS 2022
Optimal Gradient Sliding and its Application to Optimal Distributed Optimization Under Similarity NIPS 2022
The First Optimal Acceleration of High-Order Methods in Smooth Convex Optimization NIPS 2022
An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints AISTATS 2022
IntSGD: Adaptive Floatless Compression of Stochastic Gradients ICLR 2022
The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimization NIPS 2022
Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling NIPS 2022
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox NIPS 2022
ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks ICML 2021
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! AISTATS 2021
Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks NIPS 2021
Revisiting Stochastic Extragradient AISTATS 2020
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization NIPS 2020
Linearly Converging Error Compensated SGD NIPS 2020
Don’t Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop ALT 2020
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems ICML 2020
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization ICML 2020
From Local SGD to Local Fixed-Point Methods for Federated Learning ICML 2020
RSN: Randomized Subspace Newton NIPS 2019
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates NIPS 2019
Stochastic Spectral and Conjugate Descent Methods NIPS 2018