19 citations · 55 across the 11 of their papers we have counts for
4 papers · 2 filters
Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling
Cindy Trinh, Emilie Kaufmann, Claire Vernade +1
Stochastic Rank-One Bandits (Katarya et al, (2017a,b)) are a simple framework for regret minimization problems over rank-one matrices of arms. The initially proposed algorithms are…
On Multi-Armed Bandit Designs for Dose-Finding Clinical Trials
Maryam Aziz, Emilie Kaufmann, Marie-Karelle Riviere
We study the problem of finding the optimal dosage in early stage clinical trials through the multi-armed bandit lens. We advocate the use of the Thompson Sampling principle, a fle…
Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits
Lilian Besson, Emilie Kaufmann, Odalric-Ambrym Maillard +1
We introduce GLR-klUCB, a novel algorithm for the piecewise iid non-stationary bandit problem with bounded rewards. This algorithm combines an efficient bandit algorithm, kl-UCB, w…
A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players
Etienne Boursier, Emilie Kaufmann, Abbas Mehrabian +1
We study a multiplayer stochastic multi-armed bandit problem in which players cannot communicate, and if two or more players pull the same arm, a collision occurs and the involved…