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Optimality of Fast-Matching Algorithms for Random Networks With Applications to Structural Controllability., , and . IEEE Trans. Control. Netw. Syst., 4 (4): 770-780 (2017)High-Probability Regret Bounds for Bandit Online Linear Optimization., , , , , and . COLT, page 335-342. Omnipress, (2008)Deterministic MDPs with Adversarial Rewards and Bandit Feedback., , and . UAI, page 93-101. AUAI Press, (2012)Online Multiclass Boosting with Bandit Feedback., , and . CoRR, (2018)Online Multiclass Boosting with Bandit Feedback., , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 1148-1156. PMLR, (2019)Convex Calibrated Surrogates for Hierarchical Classification., , and . ICML, volume 37 of JMLR Workshop and Conference Proceedings, page 1852-1860. JMLR.org, (2015)Online Ranking with Top-1 Feedback., and . AISTATS, volume 38 of JMLR Workshop and Conference Proceedings, JMLR.org, (2015)Efficient bandit algorithms for online multiclass prediction., , and . ICML, volume 307 of ACM International Conference Proceeding Series, page 440-447. ACM, (2008)On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization, , and . (2009)Scaling Up Coordinate Descent Algorithms for Large ℓ1 Regularization Problems., , , and . ICML, icml.cc / Omnipress, (2012)