arXiv:2606. 08977v1 Announce Type: new Abstract: Motivated by the recency effect in online learning, we study algorithms for single-pass *sliding-window streaming multi-armed bandits (MABs)* in this paper.
arXiv:2602.10727v3 Announce Type: replace Abstract: Rising Multi-Armed Bandits (RMABs) model sequential decision problems where each arm's expected reward improves with repeated pulls. In such proble...
Gradient-variation online learning has attracted increasing attention for its connections to game theory and optimization. It has been studied in both non-stationary online learning, which aims to min...
arXiv:2608. 01151v1 Announce Type: cross Abstract: In this paper, we consider stochastic optimal control problems with infinite-horizon joint chance constraints.
arXiv:2610. 08745v1 Announce Type: new Abstract: We study linear bandits under exact sliding-window constraints, where every consecutive block of actions must belong to a prescribed feasible set.
arXiv:2606. 14679v1 Announce Type: new Abstract: Online inventory optimization (OIO) is online convex optimization with physical memory: inventory carryover makes the feasible action set depend on the past.
arXiv:2610. 11074v1 Announce Type: new Abstract: We establish near-optimal regret bounds for budget-constrained online learning against arbitrary classes of budget-pacing experts in the adversarial setting.
The paper addresses policy optimization in online episodic tabular MDPs with adversarial losses and bandit feedback. By employing regularized Q‑functions, the authors eliminate the extra horizon factor present in previous policy‑optimization regret bounds, achieving high‑probability regret of ≥O(√{HS(H+A)T}) for known transitions and ≥O(HS√{AT}) for unknown transitions. These results improve horizon dependence and match the best-known bounds, with extensions to adversarial linear‑mixture MDPs.
arXiv:2607. 08012v1 Announce Type: cross Abstract: This paper studies an online variant of the assistance games framework, where an informed agent and an uninformed agent repeatedly interact over $T$ timesteps to optimize a common reward function.
arXiv:2609.37660v1 Announce Type: new Abstract: We study nonpreemptive contextual queueing bandits in a single-server system. Each job is represented by a $d$-dimensional context vector; in each roun...
arXiv:2606. 04305v1 Announce Type: new Abstract: We study online learning with an additional offline dataset in the stochastic linear bandit setting.
arXiv:2510. 19528v2 Announce Type: replace-cross Abstract: We investigate the fundamental problem of leveraging offline data to accelerate online reinforcement learning - a direction with strong potential but limited theoretical grounding.