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Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems., , , , , и . J. Mach. Learn. Res., (2020)Instance-optimality in optimal value estimation: Adaptivity via variance-reduced Q-learning., , , и . CoRR, (2021)Convergence guarantees for a class of non-convex and non-smooth optimization problems., и . ICML, том 80 из Proceedings of Machine Learning Research, стр. 2606-2615. PMLR, (2018)Theoretical guarantees for EM under misspecified Gaussian mixture models., , , , и . NeurIPS, стр. 9704-9712. (2018)Convergence Guarantees for a Class of Non-convex and Non-smooth Optimization Problems., и . J. Mach. Learn. Res., (2019)Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis., , , , и . CoRR, (2020)Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models., , , , , и . AISTATS, том 108 из Proceedings of Machine Learning Research, стр. 1866-1876. PMLR, (2020)Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference., , , , и . CoRR, (2023)Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems., , , , , и . AISTATS, том 89 из Proceedings of Machine Learning Research, стр. 2916-2925. PMLR, (2019)Stochastic Optimization with Constraints: A Non-asymptotic Instance-Dependent Analysis.. CoRR, (2024)