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Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning.

, , , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 6296-6306. PMLR, (2021)

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Nonconvex Low-Rank Tensor Completion from Noisy Data., , , and . NeurIPS, page 1861-1872. (2019)Minimax Estimation of Linear Functions of Eigenvectors in the Face of Small Eigen-Gaps., , , and . IEEE Trans. Inf. Theory, 71 (2): 1200-1247 (February 2025)Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning., , , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 6296-6306. PMLR, (2021)Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis., , , , and . Oper. Res., 72 (1): 222-236 (2024)Minimax-optimal trust-aware multi-armed bandits., and . CoRR, (2024)Provable acceleration for diffusion models under minimal assumptions., and . CoRR, (2024)Structured Low-Rank Matrix Factorization for Haplotype Assembly., , and . IEEE J. Sel. Top. Signal Process., 10 (4): 647-657 (2016)Uncertainty Quantification for Nonconvex Tensor Completion: Confidence Intervals, Heteroscedasticity and Optimality., , and . IEEE Trans. Inf. Theory, 69 (1): 407-452 (2023)Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality., , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 1271-1282. PMLR, (2020)Conditional Rényi Divergence Saddlepoint and the Maximization of α-Mutual Information., and . Entropy, 21 (10): 969 (2019)