Eduard Gorbunov
35 papers · 2018–2025 · 7 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π Conference Polyglot (7) π Interdisciplinary Bridge π§ Keyword Pioneer π Academic Marathon (7)
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Keyword Pioneer
π£
Hot Topic Early Bird
π
Renaissance Researcher
(6)
π€
Dynamic Duo
(16)
π
Triple Crown
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Keyword Champion
(4)
π¬
Deep Specialist
(25)
π
Century Club
(35)
ποΈ
Keyword Collector
(101)
β‘
Prolific Year
(8)
π₯
Unstoppable
(8)
Conferences
NIPS (13)
AISTATS (7)
ICML (7)
ICLR (4)
COLT (2)
EMNLP (1)
JMLR (1)
Top co-authors
Keywords
variational inequality
(10)
stochastic optimization
(9)
stochastic gradient descent
(8)
variance reduction
(7)
convergence rate
(6)
convex optimization
(6)
distributed learning
(5)
federated learning
(5)
distributed optimization
(5)
communication compression
(4)
extragradient method
(4)
min-max optimization
(3)
heavy-tailed noise
(3)
convergence analysis
(3)
error feedback
(3)
byzantine robustness
(3)
gradient clipping
(3)
communication efficiency
(3)
distributed training
(3)
complexity bound
(2)
Papers
Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization
ICLR 2025
Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity
ICLR 2025
EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback
JMLR 2025
Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed
ICML 2025
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
ICML 2024
Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences
NIPS 2024
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad
NIPS 2024
Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations
NIPS 2024
Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences
NIPS 2024
Low-Resource Machine Translation through the Lens of Personalized Federated Learning
EMNLP 2024
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates
AISTATS 2024
Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems
AISTATS 2024
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods
AISTATS 2023
Byzantine-Tolerant Methods for Distributed Variational Inequalities
NIPS 2023
Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance
NIPS 2023
Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions
NIPS 2023
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top
ICLR 2023
Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity
ICML 2023
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance
ICML 2023
Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise
NIPS 2022
Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities
NIPS 2022
Stochastic Extragradient: General Analysis and Improved Rates
AISTATS 2022
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation
ICML 2022
Secure Distributed Training at Scale
ICML 2022
Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity
AISTATS 2022
MARINA: Faster Non-Convex Distributed Learning with Compression
ICML 2021
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices
NIPS 2021
Local SGD: Unified Theory and New Efficient Methods
AISTATS 2021
Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping
NIPS 2020
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent
AISTATS 2020
Linearly Converging Error Compensated SGD
NIPS 2020
A Stochastic Derivative Free Optimization Method with Momentum
ICLR 2020
Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives
COLT 2019
Optimal Tensor Methods in Smooth Convex and Uniformly ConvexOptimization
COLT 2019
Stochastic Spectral and Conjugate Descent Methods
NIPS 2018