8 citations · 8 across the 8 of their papers we have counts for
9 papers
Refined PAC-Bayes Bounds for Offline Bandits
Amaury Gouverneur, Tobias J. Oechtering, Mikael Skoglund
In this paper, we present refined probabilistic bounds on empirical reward estimates for off-policy learning in bandit problems. We build on the PAC-Bayesian bounds from Seldin et…
An Information-Theoretic Analysis of Thompson Sampling with Infinite Action Spaces
Amaury Gouverneur, Borja Rodriguez Gálvez, Tobias Oechtering +1
This paper studies the Bayesian regret of the Thompson Sampling algorithm for bandit problems, building on the information-theoretic framework introduced by Russo and Van Roy (2015…
An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits
Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering +1
We study the performance of the Thompson Sampling algorithm for logistic bandit problems. In this setting, an agent receives binary rewards with probabilities determined by a logis…
Information-Theoretic Minimax Regret Bounds for Reinforcement Learning based on Duality
Raghav Bongole, Amaury Gouverneur, Borja Rodríguez-Gálvez +2
We study agents acting in an unknown environment where the agent's goal is to find a robust policy. We consider robust policies as policies that achieve high cumulative rewards for…
Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems
Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering +1
This paper studies the Bayesian regret of a variant of the Thompson-Sampling algorithm for bandit problems. It builds upon the information-theoretic framework of [Russo and Van Roy…
Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards
Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering +1
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…
An Information-Theoretic Analysis of Bayesian Reinforcement Learning
Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering +1
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 learni…
Optimal Intermittent Particle Filter
Antoine Aspeel, Amaury Gouverneur, Raphaël M. Jungers +1
The problem of the optimal allocation (in the expected mean square error sense) of a measurement budget for particle filtering is addressed. We propose three different optimal inte…
Optimal measurement budget allocation for particle filtering
Antoine Aspeel, Amaury Gouverneur, Raphaël M. Jungers +1
Particle filtering is a powerful tool for target tracking. When the budget for observations is restricted, it is necessary to reduce the measurements to a limited amount of samples…