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Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds., , , and . CoRR, (2020)New lower bounds of fifteen classical Ramsey numbers., , , and . Australas. J Comb., (1999)More Adaptive Algorithms for Adversarial Bandits., and . COLT, volume 75 of Proceedings of Machine Learning Research, page 1263-1291. PMLR, (2018)Adaptive Resource Provisioning for the Cloud Using Online Bin Packing., , , and . IEEE Trans. Computers, 63 (11): 2647-2660 (2014)Variance-Reduced and Projection-Free Stochastic Optimization., and . ICML, volume 48 of JMLR Workshop and Conference Proceedings, page 1263-1271. JMLR.org, (2016)Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously., , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 7683-7692. PMLR, (2019)Achieving All with No Parameters: Adaptive NormalHedge., and . CoRR, (2015)Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs., , , and . CoRR, (2022)Learning Infinite-Horizon Average-Reward Markov Decision Processes with Constraints., , and . CoRR, (2022)Clairvoyant Regret Minimization: Equivalence with Nemirovski's Conceptual Prox Method and Extension to General Convex Games., , , and . CoRR, (2022)