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Never Worse, Mostly Better: Stable Policy Improvement in Deep Reinforcement Learning., , , , и . AAMAS, стр. 2430-2432. ACM, (2023)Tight Lower Bounds for Combinatorial Multi-Armed Bandits., и . COLT, том 125 из Proceedings of Machine Learning Research, стр. 2830-2857. PMLR, (2020)Multi-armed bandits with guaranteed revenue per arm., , , , и . AISTATS, том 238 из Proceedings of Machine Learning Research, стр. 379-387. PMLR, (2024)On Preemption and Learning in Stochastic Scheduling., , , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 24478-24516. PMLR, (2023)Reinforcement Learning with Trajectory Feedback., , и . AAAI, стр. 7288-7295. AAAI Press, (2021)On Bits and Bandits: Quantifying the Regret-Information Trade-off., , , и . CoRR, (2024)Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics., , , и . CoRR, (2023)Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning., , , , и . NeurIPS, стр. 3566-3577. (2018)Ensemble Bootstrapping for Q-Learning., , , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 8454-8463. PMLR, (2021)Reinforcement Learning with History Dependent Dynamic Contexts., , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 34011-34053. PMLR, (2023)