Aleksandr Beznosikov
22 papers · 2021–2026 · 9 venues · across top CS/AI venues
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Machine Learning
(19)
Optimization & Theory
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Mathematics & Optimization
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Optimization
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Artificial Intelligence
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Learning Paradigms · Artificial Intelligence
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Learning Paradigms · Machine Learning
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Keywords
stochastic optimization
(7)
variational inequality
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distributed learning
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variance reduction
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distributed optimization
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communication complexity
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stochastic gradient descent
(4)
convex optimization
(3)
markov chain
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error feedback
(2)
stochastic gradient
(2)
communication compression
(2)
decentralized learning
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low-rank adaptation
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extragradient method
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convergence guarantee
(2)
federated learning
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convergence rate
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gradient compression
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variational inequalities
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Papers
Bant: Byzantine Antidote via Trial Function and Trust Scores
AAAI 2026
Markovian Compression: Looking to the Past Helps Accelerate the Future
UAI 2026
Methods for Optimization Problems with Markovian Stochasticity and Non-Euclidean Geometry
AAAI 2026
WeightLoRA: Keep Only Necessary Adapters
ACL 2026
Accelerated Methods with Compressed Communications for Distributed Optimization Problems Under Data Similarity
AAAI 2025
Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed
ICML 2025
FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training
ICML 2025
Synthetic Proofs with Tool-Integrated Reasoning: Contrastive Alignment for LLM Mathematics with Lean
EMNLP 2025
When Extragradient Meets PAGE: Bridging Two Giants to Boost Variational Inequalities
UAI 2025
Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting
ICLR 2024
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features
ICML 2024
Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases
AISTATS 2024
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
NeurIPS 2023
On Biased Compression for Distributed Learning
JMLR 2023
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities
NeurIPS 2023
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods
AISTATS 2023
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities
NeurIPS 2022
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees
NeurIPS 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 power of first-order smooth optimization for black-box non-smooth problems
ICML 2022
Distributed Saddle-Point Problems Under Data Similarity
NeurIPS 2021