17 citations · 26 across the 8 of their papers we have counts for
4 papers · 1 filter
Transfer Learning in Bandits from Unknown Adaptive Policies: Problem-Dependent Lower and Upper Bounds
Adrien Prevost, Timothee Mathieu, Waris Radji +1
Transfer learning in stochastic multi-armed bandits is typically studied assuming that prior data forms a fixed offline dataset or is generated by a known source. In practice, hist…
Asymptotically Optimal Problem-Dependent Bandit Policies for Transfer Learning
Adrien Prevost, Timothee Mathieu, Odalric-Ambrym Maillard
We study the non-contextual multi-armed bandit problem in a transfer learning setting: before any pulls, the learner is given N'_k i.i.d. samples from each source distribution nu'_…
AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents
Timothée Mathieu, Riccardo Della Vecchia, Alena Shilova +4
Recently, the scientific community has questioned the statistical reproducibility of many empirical results, especially in the field of machine learning. To contribute to the resol…
Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithm
Debabrota Basu, Odalric-Ambrym Maillard, Timothée Mathieu
We study the corrupted bandit problem, i.e. a stochastic multi-armed bandit problem with unknown reward distributions, which are heavy-tailed and corrupted by a history-indepen…