AI RESEARCH
Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates
arXiv CS.LG
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ArXi:2606.00984v1 Announce Type: cross We study linear contextual bandits under rare parameter updates: the learner may incorporate reward feedback into its parameter estimate only at a small number of update times, while still observing contexts online and selecting actions sequentially. This viewpoint clarifies a practical distinction that is often blurred in the literature: many "strictly batched" methods. additionally.