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Bandits for Online Calibration: An Application to Content Moderation on Social Media Platforms.

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MNL-Bandit: A Dynamic Learning Approach to Assortment Selection., , , и . Operations Research, 67 (5): 1453-1485 (2019)Thompson Sampling for the MNL-Bandit., , , и . COLT, том 65 из Proceedings of Machine Learning Research, стр. 76-78. PMLR, (2017)Bandits for Online Calibration: An Application to Content Moderation on Social Media Platforms., , , , , , , , , и 10 other автор(ы). CoRR, (2022)Stochastic bandits for multi-platform budget optimization in online advertising., , , , и . WWW, стр. 2805-2817. ACM / IW3C2, (2021)Top K Ranking for Multi-Armed Bandit with Noisy Evaluations., , , и . AISTATS, том 151 из Proceedings of Machine Learning Research, стр. 6242-6269. PMLR, (2022)A few good predictions: selective node labeling in a social network., , и . WSDM, стр. 353-362. ACM, (2014)The MNL-Bandit Problem: Theory and Applications.. Columbia University, USA, (2019)On the tightness of an LP relaxation for rational optimization and its applications., , , и . Oper. Res. Lett., 44 (5): 612-617 (2016)MNL-Bandit: A Dynamic Learning Approach to Assortment Selection., , , и . CoRR, (2017)Robust and fair work allocation., , , , , , , , и . CoRR, (2022)