arXiv:2608. 12231v1 Announce Type: new Abstract: We study adversarial combinatorial bandits with $m$-set actions, where at each round the learner selects $m$ out of $d$ items and observes only the aggregate loss of the selected items.
By Francesco Bacchiocchi, Tommaso Cesari, Roberto Colomboni
arXiv:2609.13547v1 Announce Type: new
Abstract: We study switching regret in adversarial multi-armed bandits, where the learner competes with an arm sequence that changes at most $S$ times. When $S$...
By Mengxiao Zhang
arXiv:2607. 29460v1 Announce Type: new Abstract: Heavy-tailed distributions arise naturally in sequential decision-making problems such as financial investment, online advertising, and network management, where rare but extreme outcomes can dominate performance.
By Gianmarco Genalti, Alberto Maria Metelli
arXiv:2605. 09200v2 Announce Type: replace Abstract: We study adversarial noisy bandits given a known function class $\mathcal{F}$.
By Steve Hanneke, Kun Wang
arXiv:2606. 27448v1 Announce Type: new Abstract: This paper studies the problem of regret minimization in Markovian bandits with \emph{non-observable states} and possibly \emph{constrained} decision epochs.
By Thomas Hira, Victor Boone, Urtzi Ayesta, Ina Maria Verloop
The paper introduces Persistent Candidate LinUCB (PC‑LinUCB), a variant of the LinUCB algorithm for linear bandits that reduces per‑round score evaluations from ≥×K to ≥×(k+q) by maintaining a small set of active actions and sampling challengers from the remaining actions. It provides high‑probability regret bounds that include a standard learning term and an additive discovery cost of order ≥×((K-k)×log(K-k))/q, which does not grow with the horizon. Experiments demonstrate that PC‑LinUCB accelerates LinUCB while keeping regret comparable, and the persistent candidate approach can be applied beyond LinUCB.
By Bingkui Tong, Daria Korovaitceva, Marina Sheshukova, Sergey Samsonov, Junpei Komiyama