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Online Learning: Random Averages, Combinatorial Parameters, and Learnability.

, , and . NIPS, page 1984-1992. Curran Associates, Inc., (2010)

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On the Risk of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels., , and . CoRR, (2019)On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities., and . COLT, volume 65 of Proceedings of Machine Learning Research, page 1704-1722. PMLR, (2017)BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits., and . ICML, volume 48 of JMLR Workshop and Conference Proceedings, page 1977-1985. JMLR.org, (2016)Online Learning: Sufficient Statistics and the Burkholder Method., , and . COLT, volume 75 of Proceedings of Machine Learning Research, page 3028-3064. PMLR, (2018)Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization., , and . ICML, icml.cc / Omnipress, (2012)On Detection and Structural Reconstruction of Small-World Random Networks., , and . IEEE Trans. Network Science and Engineering, 4 (3): 165-176 (2017)Online Learning with Predictable Sequences, and . CoRR, (2012)Relax and Localize: From Value to Algorithms, , and . CoRR, (2012)Information-Based Complexity, Feedback and Dynamics in Convex Programming., and . IEEE Trans. Inf. Theory, 57 (10): 7036-7056 (2011)High-Probability Regret Bounds for Bandit Online Linear Optimization., , , , , and . COLT, page 335-342. Omnipress, (2008)