S. Samsonov

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Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize pdf

Eric Moulines, Alain Durmus, Hoi-To Wai, 2021, NeurIPS.

A. Naumov, S. Samsonov, D. Belomestny, 2020 .

Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations

A. Naumov, S. Samsonov, D. Belomestny, 2022, ArXiv.

From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses

É. Moulines, Pierre Ménard, A. Naumov, 2022, ICML.

Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation

É. Moulines, Alain Durmus, A. Naumov, 2022, 2207.04475.

BR-SNIS: Bias Reduced Self-Normalized Importance Sampling

É. Moulines, J. Olsson, S. Samsonov, 2022, ArXiv.

Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations

A. Naumov, S. Samsonov, D. Belomestny, 2022, ArXiv.

BR-SNIS: Bias Reduced Self-Normalized Importance Sampling

É. Moulines, J. Olsson, S. Samsonov, 2022, NeurIPS.

Local-Global MCMC kernels: the best of both worlds

É. Moulines, Alain Durmus, A. Naumov, 2021, NeurIPS.

Ex2MCMC: Sampling through Exploration Exploitation pdf

Eric Moulines, Denis Belomestny, Alexey Naumov, 2021, ArXiv.

Ex2MCMC: Sampling through Exploration Exploitation

É. Moulines, Daniil Selikhanovych, A. Naumov, 2021, ArXiv.

Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations

A. Naumov, S. Samsonov, D. Belomestny, 2022, Neural Networks.

Variance reduction for Markov chains with application to MCMC

É. Moulines, S. Samsonov, D. Belomestny, 2020, Statistics and computing.

Variance reduction for additive functionals of Markov chains via martingale representations

É. Moulines, S. Samsonov, D. Belomestny, 2022, Statistics and Computing.

Rates of convergence for density estimation with generative adversarial networks

É. Moulines, A. Naumov, S. Samsonov, 2021, 2102.00199.

Estimation of the Second Moment Based on Rounded Data

N. Ushakov, S. Samsonov, V. Ushakov, 2019, Journal of Mathematical Sciences.

Rates of convergence for density estimation with GANs

É. Moulines, A. Naumov, S. Samsonov, 2021 .

Theoretical guarantees for neural control variates in MCMC

A. Naumov, S. Samsonov, D. Belomestny, 2023, ArXiv.

Variance Reduction for Dependent Sequences with Applications to Stochastic Gradient MCMC

D. Belomestny, L. Iosipoi, E. Moulines, 2020, SIAM/ASA J. Uncertain. Quantification.

Variance reduction for Markov chains with application to MCMC

É. Moulines, A. Naumov, S. Samsonov, 2019, Statistics and Computing.

On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning

Eric Moulines, Alain Durmus, Hoi-To Wai, 2021, COLT.

First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities

É. Moulines, A. Gasnikov, A. Naumov, 2023, NeurIPS.

Probability and moment inequalities for additive functionals of geometrically ergodic Markov chains

É. Moulines, Alain Durmus, A. Naumov, 2021, 2109.00331.

Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation

É. Moulines, Alain Durmus, A. Naumov, 2022, Mathematics of Operations Research.

UVIP: Model-Free Approach to Evaluate Reinforcement Learning Algorithms

D. Belomestny, E. Moulines, S. Samsonov, 2021, 2105.02135.

Finite-Sample Analysis of the Temporal Difference Learning

S. Samsonov, D. Tiapkin, Alexey Naumov, 2023, ArXiv.

Rosenthal-type inequalities for linear statistics of Markov chains

É. Moulines, Alain Durmus, A. Naumov, 2023, 2303.05838.

Improving GFlowNets with Monte Carlo Tree Search

S. Samsonov, D. Tiapkin, Nikita Morozov, 2024, ArXiv.