Search Results for author:
Found 4 papers, 1 papers with code
Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems
This paper studies the Bayesian regret of a variant of the Thompson-Sampling algorithm for bandit problems.
Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards
In this work, we study the performance of the Thompson Sampling algorithm for Contextual Bandit problems based on the framework introduced by Neu et al. and their concept of lifted information ratio.
An Information-Theoretic Analysis of Bayesian Reinforcement Learning
Building on the framework introduced by Xu and Raginksy [1] for supervised learning problems, we study the best achievable performance for model-based Bayesian reinforcement learning problems.
Optimal Intermittent Particle Filter
We prove that in terms of expected mean square error, the stochastic program filter outperforms the online filter, which itself outperforms the offline filter.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
