36 citations · 48 across the 5 of their papers we have counts for
5 papers
Power Mean Estimation in Stochastic Monte-Carlo Tree_Search
Tuan Dam, Odalric-Ambrym Maillard, Emilie Kaufmann
Monte-Carlo Tree Search (MCTS) is a widely-used strategy for online planning that combines Monte-Carlo sampling with forward tree search. Its success relies on the Upper Confidence…
Towards Instance-Optimality in Online PAC Reinforcement Learning
Aymen Al-Marjani, Andrea Tirinzoni, Emilie Kaufmann
Several recent works have proposed instance-dependent upper bounds on the number of episodes needed to identify, with probability , an -optimal policy in finite-h…
Optimistic PAC Reinforcement Learning: the Instance-Dependent View
Andrea Tirinzoni, Aymen Al-Marjani, Emilie Kaufmann
Optimistic algorithms have been extensively studied for regret minimization in episodic tabular MDPs, both from a minimax and an instance-dependent view. However, for the PAC RL pr…
On the Complexity of A/B Testing
Emilie Kaufmann, Olivier Cappé, Aurélien Garivier
A/B testing refers to the task of determining the best option among two alternatives that yield random outcomes. We provide distribution-dependent lower bounds for the performance…
Thompson Sampling: An Asymptotically Optimal Finite Time Analysis
Emilie Kaufmann, Nathaniel Korda, Rémi Munos
The question of the optimality of Thompson Sampling for solving the stochastic multi-armed bandit problem had been open since 1933. In this paper we answer it positively for the ca…