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Bandit Convex Optimization: \(T\) Regret in One Dimension.

, , , and . COLT, volume 40 of JMLR Workshop and Conference Proceedings, page 266-278. JMLR.org, (2015)

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Metrical task systems on trees via mirror descent and unfair gluing., , , and . CoRR, (2018)From trees to seeds: on the inference of the seed from large trees in the uniform attachment model., , , and . CoRR, (2014)On the influence of the seed graph in the preferential attachment model., , and . CoRR, (2014)Competitively Chasing Convex Bodies., , , and . CoRR, (2018)lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits., , , and . COLT, volume 35 of JMLR Workshop and Conference Proceedings, page 423-439. JMLR.org, (2014)First-Order Regret Analysis of Thompson Sampling, and . (2019)cite arxiv:1902.00681Comment: 27 pages.Convex Optimization: Algorithms and Complexity. (2014)cite arxiv:1405.4980Comment: A previous version of the manuscript was titled "Theory of Convex Optimization for Machine Learning".Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without., , , and . COLT, volume 125 of Proceedings of Machine Learning Research, page 961-987. PMLR, (2020)Coordination without communication: optimal regret in two players multi-armed bandits., and . COLT, volume 125 of Proceedings of Machine Learning Research, page 916-939. PMLR, (2020)Cooperative and Stochastic Multi-Player Multi-Armed Bandit: Optimal Regret With Neither Communication Nor Collisions., , and . CoRR, (2020)