The paper introduces SHPRR, an algorithm that adapts Sequential Halving for pure exploration problems where regret relative to a baseline is a key cost metric. It shows that regret cost decreases as the algorithm progresses, allowing a front‑loaded allocation of the regret budget to improve best‑arm identification. Experiments on synthetic and real data demonstrate that SHPRR outperforms uniform regret allocation and existing baselines.
By Georgina Nouli, Flore Sentenac, Maximilian Schiffer
arXiv:2607. 17981v1 Announce Type: new Abstract: Representation learning has enabled classical exploration strategies to be extended to deep Reinforcement Learning (RL), but often makes algorithms more complex and theoretical guarantees harder to establish.
By Waris Radji, Odalric-Ambrym Maillard
arXiv:2409. 18909v2 Announce Type: replace Abstract: Motivated by real-world applications that necessitate responsible experimentation, we introduce the problem of best arm identification (BAI) with minimal regret.
By Junwen Yang, Vincent Y. F. Tan, Tianyuan Jin
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.
By Deqi Zheng, Xiaoyang Xu, Yuhong Yang
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.
By Pu Wang, Yao-Xiang Ding
arXiv:2606. 20107v1 Announce Type: new Abstract: Optimal Reinforcement Learning (RL) algorithms typically rely on carefully constructed count-based uncertainty estimates to drive exploration.
By Asaf Cassel, Aviv Rosenberg
arXiv:2602. 17976v2 Announce Type: replace-cross Abstract: In active sequential testing, also termed pure exploration, a learner is tasked with the goal to adaptively acquire information so as to identify an unknown ground-truth hypothesis with as few queries as possible.
By Alessio Russo, Yin-Ching Lee, Ryan Welch, Aldo Pacchiano
arXiv:2609.06489v1 Announce Type: cross
Abstract: Monte Carlo Tree Search (MCTS) has demonstrated success in online planning for deterministic environments, yet significant challenges remain in adapt...
By Tuan Dam
arXiv:2608. 01069v1 Announce Type: new Abstract: Bandit algorithms generate data for downstream inference, but adaptive sampling biases post-bandit sample means.
By Lisu Wang, Yilun Chen, Jiaqi Lu
arXiv:2610.32514v1 Announce Type: new
Abstract: Thompson sampling is a widely used strategy for sequential decision-making under uncertainty, offering a principled way to balance exploration and expl...
By Michal Kobiela, Michael U. Gutmann
arXiv:2606. 28616v1 Announce Type: new Abstract: In stochastic linear bandits, the canonical Upper Confidence Bound (UCB) algorithm admits a simple frequentist regret analysis but can be computationally demanding, while Thompson Sampling (TS) is computationally attractive yet typically harder to analyze due to its non-optimistic nature.
By Toshinori Kitamura, Shuai Liu, Csaba Szepesv\'ari
arXiv:2608.01069v2 Announce Type: replace
Abstract: Bandit algorithms generate data for downstream inference, but adaptive sampling biases post-bandit sample means. We analyze this bias for stable in...
By Lisu Wang, Yilun Chen, Jiaqi Lu