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Blackwell Approachability and Low-Regret Learning are Equivalent, , and . CoRR, (2010)Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems., , , , , and . CoRR, (2018)An Introduction to Reinforcement Learning Theory: Value Function Methods.. Machine Learning Summer School, volume 2600 of Lecture Notes in Computer Science, page 184-202. Springer, (2002)Gradient Diversity Empowers Distributed Learning., , , , , and . CoRR, (2017)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)Learning Near-optimal Convex Combinations of Basis Models with Generalization Guarantees., , and . CoRR, (2019)Sampling for Bayesian Mixture Models: MCMC with Polynomial-Time Mixing., , , , and . CoRR, (2019)Optimal Online Prediction in Adversarial Environments.. Discovery Science, volume 6332 of Lecture Notes in Computer Science, page 371. Springer, (2010)Function Learning From Interpolation., and . Comb. Probab. Comput., 9 (3): 213-225 (2000)Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks., , and . CoRR, (2018)