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Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems.

, , , , , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 2916-2925. PMLR, (2019)

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