Accelerating Linear Bandits with Persistent Candidate Sets

Abstract:Exhaustive optimistic scoring can make action selection costly in linear bandits with a large fixed set of $K$ actions, requiring $\mathcal{O}(K)$ score evaluations per round. We introduce Persistent Candidate LinUCB (\textsc{PC-LinUCB}), which maintains $k$ active actions and samples $q$ challengers uniformly from the remaining $K-k$ actions at each round. By scoring only these $k+q$ candidates, \textsc{PC-LinUCB} reduces the number of score evaluations per round to $\mathcal{O}(k+q)$. We establish problem-independent and problem-dependent high-probability regret bounds consisting of a standard linear-bandit learning term and an additive discovery cost of order $\mathcal{O}\left((K-k)\log(K-k)/q\right)$ at fixed confidence. Notably, this additional cost does not grow with the horizon. For fixed gaps and sufficiently long horizons, we establish an $\Omega\left((K-k)/q\right)$ regret lower bound under uniform challenger sampling, showing that partial candidate access itself imposes an unavoidable regret cost. Experiments show that \textsc{PC-LinUCB} substantially accelerates LinUCB while maintaining comparable regret, and that the persistent candidate mechanism can extend beyond LinUCB.

Machine Learning (cs.LG)

arXiv:2610.13407 [cs.LG]

(or arXiv:2610.13407v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.13407 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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