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Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination., and . NeurIPS, page 18907-18919. (2021)Independent Policy Gradient Methods for Competitive Reinforcement Learning., , and . NeurIPS, (2020)Parameter-free online learning via model selection., , , and . CoRR, (2018)Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression., , and . COLT, volume 195 of Proceedings of Machine Learning Research, page 4633-4656. PMLR, (2023)Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective., , , and . COLT, volume 134 of Proceedings of Machine Learning Research, page 2059. PMLR, (2021)Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information., , , , and . COLT, volume 178 of Proceedings of Machine Learning Research, page 5062-5127. PMLR, (2022)On the Complexity of Adversarial Decision Making., , , and . NeurIPS, (2022)ZigZag: A New Approach to Adaptive Online Learning., , and . COLT, volume 65 of Proceedings of Machine Learning Research, page 876-924. PMLR, (2017)Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles., and . ICML, volume 119 of Proceedings of Machine Learning Research, page 3199-3210. PMLR, (2020)Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL., , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 24659-24700. PMLR, (2023)