Alexey Naumov
20 papers · 2020–2026 · 8 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (36) 🌍 Conference Polyglot · Silver 🏃 Academic Marathon · Sprint 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
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Grand Slam
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Prolific Year · Productive
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Century Club · Bronze
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Research topics
Machine Learning
(20)
Optimization & Theory
(19)
Reinforcement Learning
(10)
Learning Types · Machine Learning
(9)
Methods
(8)
Mathematics & Optimization
(4)
Deep Learning
(3)
Models
(3)
Optimization
(3)
Artificial Intelligence
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Applications
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Bayesian & Probabilistic · Artificial Intelligence
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Learning Types · Deep Learning
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Application Areas
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Bayesian & Probabilistic · Machine Learning
(1)
Keywords
temporal difference learning
(5)
linear stochastic approximation
(5)
markovian noise
(4)
polyak-ruppert averaging
(4)
sample complexity
(3)
convergence rate
(3)
reinforcement learning
(3)
markov decision process
(3)
bayesian inference
(2)
convex optimization
(2)
policy evaluation
(2)
generative adversarial network
(2)
stochastic approximation
(2)
posterior sampling
(2)
markov chain
(2)
regret bound
(2)
two-timescale stochastic approximation
(2)
transfer learning
(1)
probabilistic modeling
(1)
matrix decomposition
(1)
Papers
Approximation Rates for Schrödinger Bridge Potentials via Fixed-Point ERM
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
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson–Romberg Extrapolation
ICLR 2025
Demonstration-Regularized RL
ICLR 2024
Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning
NeurIPS 2024
Generative Flow Networks as Entropy-Regularized RL
AISTATS 2024
Group and Shuffle: Efficient Structured Orthogonal Parametrization
NeurIPS 2024
Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability
COLT 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
Fast Rates for Maximum Entropy Exploration
ICML 2023
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
NeurIPS 2023
Model-free Posterior Sampling via Learning Rate Randomization
NeurIPS 2023
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
ICML 2022
Local-Global MCMC kernels: the best of both worlds
NeurIPS 2022
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
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
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise
COLT 2020