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Free Lunch for Optimisation under the Universal Distribution., , and . CoRR, (2016)Soft-Bayes: Prod for Mixtures of Experts with Log-Loss., , and . ALT, volume 76 of Proceedings of Machine Learning Research, page 372-399. PMLR, (2017)No Free Lunch versus Occam's Razor in Supervised Learning., and . Algorithmic Probability and Friends, volume 7070 of Lecture Notes in Computer Science, page 223-235. Springer, (2011)BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback., , , , , , and . UAI, volume 115 of Proceedings of Machine Learning Research, page 196-206. AUAI Press, (2019)On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits., and . UAI, volume 115 of Proceedings of Machine Learning Research, page 894-904. AUAI Press, (2019)Thompson Sampling is Asymptotically Optimal in General Environments., , , and . UAI, AUAI Press, (2016)Iterative Budgeted Exponential Search., , , , and . IJCAI, page 1249-1257. ijcai.org, (2019)Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits., , , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 3601-3610. PMLR, (2019)Online Learning to Rank with Features., , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 3856-3865. PMLR, (2019)Refining the Confidence Level for Optimistic Bandit Strategies.. J. Mach. Learn. Res., (2018)