Konstantin Mishchenko
19 papers · 2018–2025 · 6 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (26) 🏃 Academic Marathon · Sprint 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
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Deep Specialist
(16)
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Triple Crown
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Trend Setter
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Century Club · Bronze
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Keyword Collector
(81)
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Prolific Year · Productive
Top co-authors
Research topics
Machine Learning
(19)
Optimization & Theory · Machine Learning
(18)
Mathematics & Optimization
(9)
Optimization
(9)
Deep Learning
(7)
Optimization & Theory · Deep Learning
(5)
Artificial Intelligence
(3)
Application Areas
(3)
Learning Paradigms · Machine Learning
(3)
Learning Paradigms · Artificial Intelligence
(2)
Learning Types · Machine Learning
(2)
Computer Science
(1)
Core AI
(1)
Core Methods
(1)
Learning Types · Deep Learning
(1)
Keywords
stochastic gradient descent
(7)
convex optimization
(5)
non-convex optimization
(4)
gradient descent
(4)
distributed optimization
(3)
distributed learning
(3)
convergence rate
(3)
stochastic optimization
(3)
random reshuffling
(2)
federated learning
(2)
logistic regression
(2)
strongly convex
(2)
learning rate
(2)
nonsmooth optimization
(2)
communication efficiency
(2)
proximal gradient
(2)
variance reduction
(2)
deep learning
(1)
communication complexity
(1)
sample efficiency
(1)
Papers
Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference
EMNLP 2025
Adaptive Proximal Gradient Method for Convex Optimization
NeurIPS 2024
Prodigy: An Expeditiously Adaptive Parameter-Free Learner
ICML 2024
The Road Less Scheduled
NeurIPS 2024
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method
NeurIPS 2023
Learning-Rate-Free Learning by D-Adaptation
ICML 2023
Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy
ICML 2023
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays
NeurIPS 2022
IntSGD: Adaptive Floatless Compression of Stochastic Gradients
ICLR 2022
Proximal and Federated Random Reshuffling
ICML 2022
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!
ICML 2022
99% of Worker-Master Communication in Distributed Optimization Is Not Needed
UAI 2020
Adaptive Gradient Descent without Descent
ICML 2020
DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate
AISTATS 2020
Random Reshuffling: Simple Analysis with Vast Improvements
NeurIPS 2020
Revisiting Stochastic Extragradient
AISTATS 2020
Tighter Theory for Local SGD on Identical and Heterogeneous Data
AISTATS 2020
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning
ICML 2018
SEGA: Variance Reduction via Gradient Sketching
NeurIPS 2018