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A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem.

, , , , and . NeurIPS, page 2231-2241. (2018)

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Bayesian Exploration: Incentivizing Exploration in Bayesian Games., , , and . Oper. Res., 70 (2): 1105-1127 (2022)Locally Private Hypothesis Selection., , , , , and . COLT, volume 125 of Proceedings of Machine Learning Research, page 1785-1816. PMLR, (2020)Game-Theoretic Algorithms for Conditional Moment Matching., , , and . CoRR, (2022)Jointly Private Convex Programming., , , and . SODA, page 580-599. SIAM, (2016)Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy)., , , and . SODA, page 1890-1903. SIAM, (2015)Fair Regression: Quantitative Definitions and Reduction-Based Algorithms., , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 120-129. PMLR, (2019)Inducing Approximately Optimal Flow Using Truthful Mediators., , , and . EC, page 471-488. ACM, (2015)A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem., , , , and . NeurIPS, page 2231-2241. (2018)Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM., , , , and . NIPS, page 2566-2576. (2017)Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis., , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 9026-9035. PMLR, (2020)