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cs.LG2025
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'_…
math.ST2025
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…