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On the Complexity of Adversarial Decision Making., , , and . NeurIPS, (2022)Ticketed Learning-Unlearning Schemes., , , , , and . COLT, volume 195 of Proceedings of Machine Learning Research, page 5110-5139. PMLR, (2023)The Complexity of Making the Gradient Small in Stochastic Convex Optimization, , , , , and . (2019)cite arxiv:1902.04686.When is Agnostic Reinforcement Learning Statistically Tractable?, , , , and . CoRR, (2023)Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data., , , and . CoRR, (2024)Machine Unlearning Fails to Remove Data Poisoning Attacks., , , , , and . CoRR, (2024)Uniform Convergence of Gradients for Non-Convex Learning and Optimization., , and . NeurIPS, page 8759-8770. (2018)Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation., , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 4666-4689. PMLR, (2022)Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings., , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 34615-34641. PMLR, (2023)Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees., , , and . ICLR, OpenReview.net, (2024)