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How to Escape Saddle Points Efficiently.

, , , , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 1724-1732. PMLR, (2017)

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Open Problem: Do Good Algorithms Necessarily Query Bad Points?, , , , , and . COLT, volume 99 of Proceedings of Machine Learning Research, page 3190-3193. PMLR, (2019)Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control., , , , and . ICLR (Poster), OpenReview.net, (2019)Deterministic Calibration and Nash Equilibrium., and . COLT, volume 3120 of Lecture Notes in Computer Science, page 33-48. Springer, (2004)Maximum Likelihood Estimation for Learning Populations of Parameters., , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 6448-6457. PMLR, (2019)Learning Mixtures of Gaussians in High Dimensions., , and . STOC, page 761-770. ACM, (2015)The Value of Observation for Monitoring Dynamic Systems., , and . IJCAI, page 2474-2479. (2007)Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?, , , and . (2019)cite arxiv:1910.03016.The Nonstochastic Control Problem, , and . (2019)cite arxiv:1911.12178Comment: To appear at Algorithmic Learning Theory (ALT) 2020; small revisions from the last ver.Online Control with Adversarial Disturbances, , , , and . (2019)cite arxiv:1902.08721.Stochastic Linear Optimization under Bandit Feedback., , and . COLT, page 355-366. Omnipress, (2008)