arXiv Machine Learning
Cascading Bandits with Noisy Observations: Algorithms and Regret Bounds
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arXiv Machine Learning
Oct 2
Rising Multi-Armed Bandits with Known Horizons
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...
arXiv Machine Learning
Jun 2
Tree-Guided Identify-Then-Exploit: A Unified Framework of Best Arm Identification and Regret Minimization for Dueling Bandits
arXiv:2606. 01799v1 Announce Type: new Abstract: We study $N$-armed stochastic dueling bandits under the Condorcet-winner assumption, where three widely adopted objectives are considered: best-arm identification (BAI), weak regret, and strong regret.
arXiv Machine Learning
Jun 9
Multi-Armed Bandits with Arriving Arms: Sequential Screening, Dynamic Regret, and Sublinear Guarantees
arXiv:2606. 09002v1 Announce Type: cross Abstract: We study a stochastic multi-armed bandit problem in which the set of available arms expands over time.
arXiv Machine Learning
Oct 12
Accelerating Linear Bandits with Persistent Candidate Sets
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.
arXiv Machine Learning
Sep 30
Learning When to Update: A Near-Optimal Timing Bandit Approach
arXiv:2609.37932v1 Announce Type: new Abstract: Systems operating in dynamic environments require timely updates to sustain performance. For resource-intensive systems such as machine learning models...
arXiv Machine Learning
18h ago
The Price of Current-Effect Inference in Nonstationary Multi-Armed Bandits
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.