Sergey Samsonov
18 papers · 2021–2026 · 7 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+10 more ↓ Show less ↑
🏃 Academic Marathon (5) 🧭 Keyword Pioneer 🐝 Cross-Pollinator (12) 🌍 Conference Polyglot (6) 🌈 Renaissance Researcher (5)
🌉
Interdisciplinary Bridge
🐝
Cross-Pollinator
(12)
🐣
Hot Topic Early Bird
🏆
Grand Slam
🤝
Dynamic Duo
(13)
🏆
Keyword Champion
(4)
💎
Century Club
(15)
⚡
Prolific Year
(5)
🗃️
Keyword Collector
(68)
🔥
Unstoppable
(5)
Conferences
NIPS (6)
AAAI (3)
AISTATS (2)
COLT (2)
ICLR (2)
ICML (2)
JMLR (1)
Top co-authors
Keywords
temporal difference learning
(5)
linear stochastic approximation
(5)
polyak-ruppert averaging
(4)
markovian noise
(2)
markov decision process
(2)
stochastic approximation
(2)
monte carlo method
(2)
federated learning
(2)
generative adversarial network
(2)
dirichlet process
(1)
markov chain monte carlo
(1)
distributed learning
(1)
policy evaluation
(1)
nonparametric estimation
(1)
image classification
(1)
minimax optimality
(1)
density estimation
(1)
markov chain
(1)
gaussian approximation
(1)
bayesian inference
(1)
Papers
High-Order Error Bounds for Markovian LSA with Richardson–Romberg Extrapolation
AAAI 2026
Gaussian Approximation for Two-Timescale Linear Stochastic Approximation
AAAI 2026
Matrix-Free Two-to-Infinity and One-to-Two Norms Estimation
AAAI 2026
Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation
AISTATS 2025
Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization
ICLR 2025
Revisiting Non-Acyclic GFlowNets in Discrete Environments
ICML 2025
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson–Romberg Extrapolation
ICLR 2025
Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability
COLT 2024
Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning
NIPS 2024
SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
NIPS 2024
Queuing dynamics of asynchronous Federated Learning
AISTATS 2024
Rates of convergence for density estimation with generative adversarial networks
JMLR 2024
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
NIPS 2023
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
ICML 2022
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling
NIPS 2022
Local-Global MCMC kernels: the best of both worlds
NIPS 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
NIPS 2021