no code implementations • 6 Feb 2024 • Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, REDA ALAMI, Alexey Naumov, Eric Moulines
In this paper, we perform a non-asymptotic analysis of the federated linear stochastic approximation (FedLSA) algorithm.
no code implementations • 22 Oct 2023 • Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines
In this paper we consider the problem of obtaining sharp bounds for the performance of temporal difference (TD) methods with linear functional approximation for policy evaluation in discounted Markov Decision Processes.
no code implementations • NeurIPS 2023 • Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova, Alexander Gasnikov, Alexey Naumov, Eric Moulines
We present a unified approach for the theoretical analysis of first-order gradient methods for stochastic optimization and variational inequalities.
no code implementations • 3 Apr 2023 • Denis Belomestny, Artur Goldman, Alexey Naumov, Sergey Samsonov
In this paper, we propose a variance reduction approach for Markov chains based on additive control variates and the minimization of an appropriate estimate for the asymptotic variance.
no code implementations • 10 Mar 2023 • Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Marina Sheshukova
In this paper, we establish novel deviation bounds for additive functionals of geometrically ergodic Markov chains similar to Rosenthal and Bernstein inequalities for sums of independent random variables.
1 code implementation • 13 Jul 2022 • Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson
This method is a wrapper in the sense that it uses the same proposal samples and importance weights as SNIS, but makes clever use of iterated sampling--importance resampling (ISIR) to form a bias-reduced version of the estimator.
no code implementations • 10 Jul 2022 • Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov
Our finite-time instance-dependent bounds for the averaged LSA iterates are sharp in the sense that the leading term we obtain coincides with the local asymptotic minimax limit.
no code implementations • 16 May 2022 • Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Menard
We propose the Bayes-UCBVI algorithm for reinforcement learning in tabular, stage-dependent, episodic Markov decision process: a natural extension of the Bayes-UCB algorithm by Kaufmann et al. (2012) for multi-armed bandits.
1 code implementation • 4 Nov 2021 • Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines
Recent works leveraging learning to enhance sampling have shown promising results, in particular by designing effective non-local moves and global proposals.
no code implementations • NeurIPS 2021 • Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai
This family of methods arises in many machine learning tasks and is used to obtain approximate solutions of a linear system $\bar{A}\theta = \bar{b}$ for which $\bar{A}$ and $\bar{b}$ can only be accessed through random estimates $\{({\bf A}_n, {\bf b}_n): n \in \mathbb{N}^*\}$.
no code implementations • 30 Jan 2021 • Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov
In this work we undertake a thorough study of the non-asymptotic properties of the vanilla generative adversarial networks (GANs).
no code implementations • 30 Jan 2021 • Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Hoi-To Wai
This paper studies the exponential stability of random matrix products driven by a general (possibly unbounded) state space Markov chain.