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Found 12 papers, 2 papers with code
SCAFFLSA: Quantifying and Eliminating Heterogeneity Bias in Federated Linear Stochastic Approximation and Temporal Difference Learning
In this paper, we perform a non-asymptotic analysis of the federated linear stochastic approximation (FedLSA) algorithm.
Finite-Sample Analysis of the Temporal Difference Learning
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.
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
We present a unified approach for the theoretical analysis of first-order gradient methods for stochastic optimization and variational inequalities.
Theoretical guarantees for neural control variates in MCMC
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.
Rosenthal-type inequalities for linear statistics of Markov chains
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.
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling
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.
Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation
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.
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
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.
Local-Global MCMC kernels: the best of both worlds
Recent works leveraging learning to enhance sampling have shown promising results, in particular by designing effective non-local moves and global proposals.
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
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 $\{({\bf A}_n, {\bf b}_n): n \in \mathbb{N}^*\}$.
Rates of convergence for density estimation with generative adversarial networks
In this work we undertake a thorough study of the non-asymptotic properties of the vanilla generative adversarial networks (GANs).
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 driven by a general (possibly unbounded) state space Markov chain.
