Alexey Naumov | DeepAI

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∙ 04/06/2023

Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithms

In this work, we derive sharp non-asymptotic deviation bounds for weight...

0 Denis Belomestny, et al. ∙

∙ 04/03/2023

Theoretical guarantees for neural control variates in MCMC

In this paper, we propose a variance reduction approach for Markov chain...

0 Denis Belomestny, et al. ∙

∙ 03/14/2023

Fast Rates for Maximum Entropy Exploration

We consider the reinforcement learning (RL) setting, in which the agent ...

0 Daniil Tiapkin, et al. ∙

∙ 03/10/2023

Rosenthal-type inequalities for linear statistics of Markov chains

In this paper, we establish novel deviation bounds for additive function...

0 Alain Durmus, et al. ∙

∙ 09/28/2022

Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees

We consider reinforcement learning in an environment modeled by an episo...

0 Daniil Tiapkin, et al. ∙

∙ 07/10/2022

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

This paper provides a finite-time analysis of linear stochastic approxim...

0 Alain Durmus, et al. ∙

∙ 06/20/2022

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

This paper investigates the approximation properties of deep neural netw...

0 Denis Belomestny, et al. ∙

∙ 05/16/2022

From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses

We propose the Bayes-UCBVI algorithm for reinforcement learning in tabul...

0 Daniil Tiapkin, et al. ∙

∙ 11/04/2021

Ex^2MCMC: Sampling through Exploration Exploitation

We develop an Explore-Exploit Markov chain Monte Carlo algorithm (Ex^2MC...

0 Evgeny Lagutin, et al. ∙

∙ 06/02/2021

Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize

This paper provides a non-asymptotic analysis of linear stochastic appro...

0 Alain Durmus, et al. ∙

∙ 01/30/2021

Rates of convergence for density estimation with GANs

We undertake a precise study of the non-asymptotic properties of vanilla...

0 Denis Belomestny, et al. ∙

∙ 01/30/2021

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

This paper studies the exponential stability of random matrix products d...

0 Alain Durmus, et al. ∙

∙ 02/04/2020

Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise

Linear two-timescale stochastic approximation (SA) scheme is an importan...

0 Maxim Kaledin, et al. ∙

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