Grigory Malinovsky
13 papers · 2022–2026 · 6 venues · across top CS/AI venues
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Achievements
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🐣 Hot Topic Early Bird 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Bronze 🗺️ Taxonomy Completionist (22) 🤝 Dynamic Duo (12)
🏆
Grand Slam
🔬
Deep Specialist
(11)
❓
The Questioner
🔥
Unstoppable · Bronze
💎
Century Club · Bronze
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Keyword Collector
(55)
Top co-authors
Research topics
Machine Learning
(13)
Optimization & Theory · Machine Learning
(11)
Mathematics & Optimization
(8)
Optimization
(8)
Deep Learning
(6)
Learning Paradigms · Machine Learning
(6)
Optimization & Theory · Deep Learning
(5)
Artificial Intelligence
(4)
Learning Paradigms · Artificial Intelligence
(4)
Learning Types · Machine Learning
(2)
Application Areas
(1)
Core AI
(1)
Learning Types · Deep Learning
(1)
Keywords
federated learning
(7)
variance reduction
(5)
stochastic optimization
(4)
stochastic gradient descent
(4)
random reshuffling
(3)
convergence guarantee
(3)
gradient clipping
(3)
gradient compression
(2)
local training
(2)
neural network optimization
(2)
distributed optimization
(2)
client sampling
(2)
communication efficiency
(2)
communication complexity
(2)
byzantine robustness
(2)
convergence rate
(2)
nonsmooth optimization
(1)
convergence analysis
(1)
distributed training
(1)
non-convex optimization
(1)
Papers
Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions
UAI 2026
An Optimal Algorithm for Strongly Convex Min-Min Optimization
UAI 2025
MAST: model-agnostic sparsified training
ICLR 2025
Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization
ICLR 2025
Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences
NeurIPS 2024
Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences
NeurIPS 2024
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence
NeurIPS 2024
Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization
AAAI 2024
A Guide Through the Zoo of Biased SGD
NeurIPS 2023
Can 5th Generation Local Training Methods Support Client Sampling? Yes!
AISTATS 2023
Random Reshuffling with Variance Reduction: New Analysis and Better Rates
UAI 2023
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!
ICML 2022
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning
NeurIPS 2022