Sergey Samsonov
19 papers · 2021–2026 · 8 venues · across top CS/AI venues
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🗺️ Taxonomy Completionist (26) 🌍 Conference Polyglot · Silver 🏃 Academic Marathon · Sprint 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
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Research topics
Machine Learning
(17)
Optimization & Theory
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Reinforcement Learning
(5)
Mathematics & Optimization
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Learning Types
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Deep Learning
(3)
Methods
(3)
Optimization
(3)
Applications
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Models
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Artificial Intelligence
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Application Areas
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Bayesian & Probabilistic · Artificial Intelligence
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Bayesian & Probabilistic · Machine Learning
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Core Methods
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Keywords
temporal difference learning
(5)
linear stochastic approximation
(5)
polyak-ruppert averaging
(4)
markovian noise
(3)
federated learning
(3)
convergence rate
(3)
markov decision process
(3)
stochastic approximation
(3)
richardson-romberg extrapolation
(2)
multiplier bootstrap
(2)
monte carlo method
(2)
gaussian approximation
(2)
generative adversarial network
(2)
convex optimization
(2)
markov chain
(2)
markov chain monte carlo
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distributed learning
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policy evaluation
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minimax optimality
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sample complexity
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Papers
Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation
UAI 2026
Gaussian Approximation for Two-Timescale Linear Stochastic Approximation
AAAI 2026
High-Order Error Bounds for Markovian LSA with Richardson–Romberg Extrapolation
AAAI 2026
Matrix-Free Two-to-Infinity and One-to-Two Norms Estimation
AAAI 2026
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson–Romberg Extrapolation
ICLR 2025
Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization
ICLR 2025
Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation
AISTATS 2025
Revisiting Non-Acyclic GFlowNets in Discrete Environments
ICML 2025
Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning
NeurIPS 2024
Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability
COLT 2024
Queuing dynamics of asynchronous Federated Learning
AISTATS 2024
Rates of convergence for density estimation with generative adversarial networks
JMLR 2024
SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
NeurIPS 2024
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
NeurIPS 2023
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling
NeurIPS 2022
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
ICML 2022
Local-Global MCMC kernels: the best of both worlds
NeurIPS 2022
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning
COLT 2021
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
NeurIPS 2021