Alexey Naumov
19 papers · 2020–2026 · 7 conferences · across top CS/AI conferences
Achievements
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(8)
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(15)
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Conferences
NIPS (8)
COLT (3)
AAAI (2)
ICLR (2)
ICML (2)
AISTATS (1)
JMLR (1)
Top co-authors
Keywords
temporal difference learning
(5)
linear stochastic approximation
(5)
polyak-ruppert averaging
(4)
markovian noise
(3)
markov decision process
(3)
bayesian inference
(2)
stochastic approximation
(2)
convergence rate
(2)
generative adversarial network
(2)
two-timescale stochastic approximation
(2)
policy evaluation
(2)
posterior sampling
(2)
regret bound
(2)
density estimation
(1)
reinforcement learning
(1)
convex optimization
(1)
transfer learning
(1)
matrix decomposition
(1)
stochastic optimization
(1)
probabilistic modeling
(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
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson–Romberg Extrapolation
ICLR 2025
Rates of convergence for density estimation with generative adversarial networks
JMLR 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
Group and Shuffle: Efficient Structured Orthogonal Parametrization
NIPS 2024
Generative Flow Networks as Entropy-Regularized RL
AISTATS 2024
Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability
COLT 2024
Demonstration-Regularized RL
ICLR 2024
Model-free Posterior Sampling via Learning Rate Randomization
NIPS 2023
Fast Rates for Maximum Entropy Exploration
ICML 2023
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
NIPS 2023
Local-Global MCMC kernels: the best of both worlds
NIPS 2022
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
ICML 2022
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
NIPS 2022
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
NIPS 2021
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning
COLT 2021
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise
COLT 2020