AI RESEARCH
Sample-Mean Anchored Thompson Sampling for Offline-to-Online Learning with Distribution Shift
arXiv CS.LG
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ArXi:2605.10289v1 Announce Type: new Offline-to-online learning aims to improve online decision-making by leveraging offline logged data. A central challenge in this setting is the distribution shift between offline and online environments. While some existing works attempt to leverage shifted offline data, they largely rely on UCB-type algorithms. Thompson sampling (TS) represents another canonical class of bandit algorithms, well known for its strong empirical performance and naturally suited to offline-to-online learning through its Bayesian formulation.