17 citations · 26 across the 8 of their papers we have counts for
10 papers
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…
Optimal Posterior E-values with Non-Convex Parameter Sets with Applications to Voting Systems
Adrienne Tuynman, Timothée Mathieu
We are interested in conducting political polls sequentially, so that one can stop acquiring data as soon as possible while safely yielding statistically significant results. Build…
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'_…
Visual tests using several safe confidence intervals
Timothée Mathieu
We propose a new statistical hypothesis testing framework which decides visually, using confidence intervals, whether the means of two samples are equal or if one is larger than th…
Information Lower Bounds for Robust Mean Estimation
Rémy Degenne, Timothée Mathieu
We prove lower bounds on the error of any estimator for the mean of a real probability distribution under the knowledge that the distribution belongs to a given set. We apply these…
CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption
Shubhada Agrawal, Timothée Mathieu, Debabrota Basu +1
We investigate the regret-minimisation problem in a multi-armed bandit setting with arbitrary corruptions. Similar to the classical setup, the agent receives rewards generated inde…
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…
Concentration study of M-estimators using the influence function
Timothée Mathieu
We present a new finite-sample analysis of M-estimators of locations in using the tool of the influence function. In particular, we show that the deviations of an M-…
Excess risk bounds in robust empirical risk minimization
Stanislav Minsker, Timothée Mathieu
This paper investigates robust versions of the general empirical risk minimization algorithm, one of the core techniques underlying modern statistical methods. Success of the empir…