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
The paper investigates transfer learning in stochastic multi‑armed bandits when historical data come from an unknown adaptive policy. It establishes asymptotic problem‑dependent lower bounds and introduces the KL‑UCB‑Transfer algorithm, which combines online and historical data to achieve logarithmic regret with an improved constant under certain mean‑gap conditions. The study also relaxes the source‑target discrepancy assumption to a one‑sided condition, demonstrating that logarithmic regret can still be achieved under misspecification and identifying regimes of improvement, degradation, or loss of guarantee.
By Adrien Prevost, Timothee Mathieu, Waris Radji, Odalric-Ambrym Maillard
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 studies the trade‑off between maximizing rewards and accurately estimating current treatment effects in nonstationary multi‑armed bandits. For $K$‑armed bandits with H"older‑smooth mean rewards, the authors derive minimax bounds on expected dynamic regret under a high‑probability simultaneous coverage requirement for all current effects. They propose the TRACE algorithm, which achieves a regret–precision trade‑off that separates reward‑learning regret from the unavoidable cost of refreshing current‑effect estimates, and extend the method with an empirical‑Bernstein e‑process to adapt exploration while preserving worst‑case guarantees.
By Sharan Sahu, Martin T. Wells
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
arXiv:2606. 00835v1 Announce Type: new Abstract: Network routers that enforce Quality-of-Service (QoS) guarantees must decide, at every clock cycle, which expiring packet of information to transmit, even when the value of the packet is unknown until it is processed.
By Gianmarco Genalti, Achraf Azize, Vianney Perchet