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Proximal Newton-type Methods for Minimizing Convex Objective Functions in Composite Form, , and . CoRR, (2012)Gradient Descent Finds Global Minima of Deep Neural Networks., , , , and . CoRR, (2018)Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems., , , , and . NeurIPS, (2022)Optimal Gradient-based Algorithms for Non-concave Bandit Optimization., , , , , , and . NeurIPS, page 29101-29115. (2021)Provably Correct Automatic Sub-Differentiation for Qualified Programs., and . NeurIPS, page 7125-7135. (2018)How Fine-Tuning Allows for Effective Meta-Learning., , and . NeurIPS, page 8871-8884. (2021)Offline Reinforcement Learning with Realizability and Single-policy Concentrability., , , , and . COLT, volume 178 of Proceedings of Machine Learning Research, page 2730-2775. PMLR, (2022)Towards Understanding Hierarchical Learning: Benefits of Neural Representations., , , , , , and . NeurIPS, (2020)Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot., , , , , , and . NeurIPS, (2020)Agnostic $Q$-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity., , , and . NeurIPS, (2020)