26 citations · 69 across the 10 of their papers we have counts for
10 papers
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello +6
We consider reinforcement learning in an environment modeled by an episodic, finite, stage-dependent Markov decision process of horizon with states, and actions. The pe…
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
Alain Durmus, Eric Moulines, Alexey Naumov +3
This paper provides a non-asymptotic analysis of linear stochastic approximation (LSA) algorithms with fixed stepsize. This family of methods arises in many machine learning tasks…
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning
Alain Durmus, Eric Moulines, Alexey Naumov +2
This paper studies the exponential stability of random matrix products driven by a general (possibly unbounded) state space Markov chain. It is a cornerstone in the analysis of sto…
Two-sided inequalities for the density function's maximum of weighted sum of chi-square variables
Sergey G. Bobkov, Alexey A. Naumov, Vladimir V. Ulyanov
Two--sided bounds are constructed for a probability density function of a weighted sum of chi-square variables. Both cases of central and non-central chi-square variables are consi…
Variance reduction for dependent sequences with applications to Stochastic Gradient MCMC
D. Belomestny, L. Iosipoi, E. Moulines +2
In this paper we propose a novel and practical variance reduction approach for additive functionals of dependent sequences. Our approach combines the use of control variates with t…
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise
Maxim Kaledin, Eric Moulines, Alexey Naumov +2
Linear two-timescale stochastic approximation (SA) scheme is an important class of algorithms which has become popular in reinforcement learning (RL), particularly for the policy e…