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Upper-Confidence-Bound Algorithms for Active Learning in Multi-armed Bandits.

, , , , and . ALT, volume 6925 of Lecture Notes in Computer Science, page 189-203. Springer, (2011)

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Tight (Lower) Bounds for the Fixed Budget Best Arm Identification Bandit Problem., and . COLT, volume 49 of JMLR Workshop and Conference Proceedings, page 590-604. JMLR.org, (2016)Finite Time Analysis of Stratified Sampling for Monte Carlo., and . NIPS, page 1278-1286. (2011)Online Learning with Feedback Graphs: The True Shape of Regret., and . ICML, volume 202 of Proceedings of Machine Learning Research, page 17260-17282. PMLR, (2023)Linear bandits with Stochastic Delayed Feedback., , , , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 9712-9721. PMLR, (2020)A simple and improved algorithm for noisy, convex, zeroth-order optimisation.. CoRR, (2024)Simple regret for infinitely many armed bandits., and . ICML, volume 37 of JMLR Workshop and Conference Proceedings, page 1133-1141. JMLR.org, (2015)The price of unfairness in linear bandits with biased feedback., , and . NeurIPS, (2022)Extreme bandits., and . NIPS, page 1089-1097. (2014)An optimal algorithm for the Thresholding Bandit Problem., , and . ICML, volume 48 of JMLR Workshop and Conference Proceedings, page 1690-1698. JMLR.org, (2016)Toward Optimal Stratification for Stratified Monte-Carlo Integration., and . ICML (2), volume 28 of JMLR Workshop and Conference Proceedings, page 28-36. JMLR.org, (2013)