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cs.LG2026
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…
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'_…