Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates
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 restrict within-interval context adaptivity, meaning that the action rule inside an interval cannot depend on the sequence of realized contexts/actions in that interval (beyond the current round's context). For linear contextual bandits, we propose two practical algorithms with only $O(\log\log T)$ parameter u...
| Authors | Sanghoon Yu, Min-hwan Oh |
|---|---|
| Primary category | stat.ML |
| Published | 31 May 2026 |
| Last updated at source | 22 August 2026 |
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