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Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks.

, , , и . CoRR, (2018)

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Zhou Zhou

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Yaming Zhou

 

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Minimax Optimal Reinforcement Learning for Discounted MDPs., , и . CoRR, (2020)Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks., , , и . CoRR, (2018)Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL., , , , и . UAI, том 216 из Proceedings of Machine Learning Research, стр. 2488-2497. PMLR, (2023)Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization., , и . NeurIPS, стр. 3925-3936. (2018)Logarithmic Regret for Reinforcement Learning with Linear Function Approximation., , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 4171-4180. PMLR, (2021)Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization., , и . AISTATS, том 108 из Proceedings of Machine Learning Research, стр. 4430-4440. PMLR, (2020)Gradient descent optimizes over-parameterized deep ReLU networks., , , и . Mach. Learn., 109 (3): 467-492 (2020)Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes., , и . CoRR, (2020)Learning Contextual Bandits Through Perturbed Rewards., , , , и . CoRR, (2022)Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation., , и . CoRR, (2021)