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Optimal discovery with probabilistic expert advice: finite time analysis and macroscopic optimality.

, , and . J. Mach. Learn. Res., 14 (1): 601-623 (2013)

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Convex Optimization: Algorithms and Complexity. (2015)cite arxiv:1405.4980Comment: A previous version of the manuscript was titled "Theory of Convex Optimization for Machine Learning".Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits., , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 186-194. PMLR, (2018)Conference on Learning Theory 2018: Preface., and . COLT, volume 75 of Proceedings of Machine Learning Research, page 1. PMLR, (2018)Near-optimal method for highly smooth convex optimization., , , , and . COLT, volume 99 of Proceedings of Machine Learning Research, page 492-507. PMLR, (2019)Bounded regret in stochastic multi-armed bandits., , and . COLT, volume 30 of JMLR Workshop and Conference Proceedings, page 122-134. JMLR.org, (2013)Pure Exploration in Multi-armed Bandits Problems., , and . ALT, volume 5809 of Lecture Notes in Computer Science, page 23-37. Springer, (2009)Local max-cut in smoothed polynomial time., , , and . STOC, page 429-437. ACM, (2017)Kernel-based methods for bandit convex optimization., , and . STOC, page 72-85. ACM, (2017)On Finding the Largest Mean Among Many., , , and . CoRR, (2013)Testing for high-dimensional geometry in random graphs., , , and . CoRR, (2014)