Dmitry Kovalev
26 papers · 2018–2025 · 6 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (25) 🏃 Academic Marathon · Sprint 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
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Deep Specialist
(16)
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Dynamic Duo
(18)
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Topic Evolution
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Keyword Champion
(5)
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Keyword Collector
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The Questioner
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Unstoppable · Bronze
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Century Club · Bronze
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Prolific Year · Productive
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Research topics
Machine Learning
(22)
Mathematics & Optimization
(20)
Optimization
(20)
Optimization & Theory · Machine Learning
(20)
Artificial Intelligence
(4)
Deep Learning
(4)
Learning Paradigms · Artificial Intelligence
(4)
Learning Types · Machine Learning
(2)
Learning Types · Deep Learning
(2)
Optimization & Theory · Deep Learning
(2)
Computer Science
(1)
Application Areas
(1)
Learning Paradigms · Machine Learning
(1)
Systems
(1)
Keywords
convex optimization
(11)
decentralized optimization
(8)
distributed learning
(6)
communication complexity
(6)
distributed optimization
(6)
variance reduction
(5)
optimal algorithm
(5)
linear convergence
(4)
federated learning
(4)
stochastic optimization
(3)
time-varying network
(3)
oracle complexity
(3)
saddle-point problems
(3)
gradient evaluation
(3)
stochastic gradient descent
(2)
convex-concave saddle-point
(2)
strongly convex optimization
(2)
strong convexity
(2)
variational inequality
(2)
primal-dual algorithm
(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
NeurIPS 2024
Is Consensus Acceleration Possible in Decentralized Optimization over Slowly Time-Varying Networks?
ICML 2023
Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling
NeurIPS 2022
An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints
AISTATS 2022
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox
NeurIPS 2022
IntSGD: Adaptive Floatless Compression of Stochastic Gradients
ICLR 2022
Optimal Algorithms for Decentralized Stochastic Variational Inequalities
NeurIPS 2022
Optimal Gradient Sliding and its Application to Optimal Distributed Optimization Under Similarity
NeurIPS 2022
The First Optimal Acceleration of High-Order Methods in Smooth Convex Optimization
NeurIPS 2022
The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimization
NeurIPS 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
NeurIPS 2021
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization
ICML 2020
Don’t Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop
ALT 2020
From Local SGD to Local Fixed-Point Methods for Federated Learning
ICML 2020
Linearly Converging Error Compensated SGD
NeurIPS 2020
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization
NeurIPS 2020
Revisiting Stochastic Extragradient
AISTATS 2020
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems
ICML 2020
RSN: Randomized Subspace Newton
NeurIPS 2019
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates
NeurIPS 2019
Stochastic Spectral and Conjugate Descent Methods
NeurIPS 2018