Pierre Perrault | DeepAI

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∙ 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. ∙

∙ 02/22/2023

When Combinatorial Thompson Sampling meets Approximation Regret

We study the Combinatorial Thompson Sampling policy (CTS) for combinator...

0 Pierre Perrault, et al. ∙

∙ 01/05/2021

On the Approximation Relationship between Optimizing Ratio of Submodular (RS) and Difference of Submodular (DS) Functions

We demonstrate that from an algorithm guaranteeing an approximation fact...

0 Pierre Perrault, et al. ∙

∙ 06/11/2020

Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits

We investigate stochastic combinatorial multi-armed bandit with semi-ban...

0 Pierre Perrault, et al. ∙

∙ 06/20/2019

Active Linear Regression

We consider the problem of active linear regression where a decision mak...

0 Xavier Fontaine, et al. ∙

∙ 02/11/2019

Exploiting Structure of Uncertainty for Efficient Combinatorial Semi-Bandits

We improve the efficiency of algorithms for stochastic combinatorial sem...

0 Pierre Perrault, et al. ∙

∙ 06/06/2018

Finding the Bandit in a Graph: Sequential Search-and-Stop

We consider the problem where an agent wants to find a hidden object tha...

0 Pierre Perrault, et al. ∙

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