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Ranking by calibrated AdaBoost.

, , , and . Yahoo! Learning to Rank Challenge, volume 14 of JMLR Proceedings, page 37-48. JMLR.org, (2011)

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Fast boosting using adversarial bandits., and . ICML, page 143-150. Omnipress, (2010)A Robust Ranking Methodology Based on Diverse Calibration of AdaBoost., , , and . ECML/PKDD (1), volume 6911 of Lecture Notes in Computer Science, page 263-279. Springer, (2011)A Survey of Preference-Based Online Learning with Bandit Algorithms., and . ALT, volume 8776 of Lecture Notes in Computer Science, page 18-39. Springer, (2014)Multi-objective Bandits: Optimizing the Generalized Gini Index., , , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 625-634. PMLR, (2017)PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences., , and . AAAI, page 1701-1707. AAAI Press, (2014)Learning to Rank Lexical Substitutions., , and . EMNLP, page 1926-1932. ACL, (2013)Determining Native Language and Deception Using Phonetic Features and Classifier Combination., , , and . INTERSPEECH, page 2418-2422. ISCA, (2016)Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers., , , and . Mach. Learn., 93 (2-3): 261-292 (2013)Preference-based Online Learning with Dueling Bandits: A Survey., , , and . J. Mach. Learn. Res., (2021)Preference-based Online Learning with Dueling Bandits: A Survey., , and . CoRR, (2018)