Dongruo Zhou
Dongruo Zhou
Verified email at cs.ucla.edu - Homepage
Title
Cited by
Cited by
Year
Gradient descent optimizes over-parameterized deep ReLU networks
D Zou, Y Cao, D Zhou, Q Gu
Machine Learning 109 (3), 467-492, 2020
3502020
Stochastic nested variance reduction for nonconvex optimization
D Zhou, P Xu, Q Gu
arXiv preprint arXiv:1806.07811, 2018
1172018
Closing the generalization gap of adaptive gradient methods in training deep neural networks
J Chen, D Zhou, Y Tang, Z Yang, Y Cao, Q Gu
arXiv preprint arXiv:1806.06763, 2018
862018
On the convergence of adaptive gradient methods for nonconvex optimization
D Zhou, J Chen, Y Cao, Y Tang, Z Yang, Q Gu
arXiv preprint arXiv:1808.05671, 2018
812018
Neural contextual bandits with UCB-based exploration
D Zhou, L Li, Q Gu
International Conference on Machine Learning, 11492-11502, 2020
45*2020
Stochastic Variance-Reduced Cubic Regularization Methods.
D Zhou, P Xu, Q Gu
J. Mach. Learn. Res. 20 (134), 1-47, 2019
45*2019
Provably efficient reinforcement learning for discounted mdps with feature mapping
D Zhou, J He, Q Gu
International Conference on Machine Learning, 12793-12802, 2021
382021
A Frank-Wolfe framework for efficient and effective adversarial attacks
J Chen, D Zhou, J Yi, Q Gu
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3486-3494, 2020
352020
Stochastic nested variance reduction for nonconvex optimization
D Zhou, P Xu, Q Gu
Journal of machine learning research, 2020
25*2020
Nearly minimax optimal reinforcement learning for linear mixture markov decision processes
D Zhou, Q Gu, C Szepesvari
Conference on Learning Theory, 4532-4576, 2021
232021
Lower bounds for smooth nonconvex finite-sum optimization
D Zhou, Q Gu
International Conference on Machine Learning, 7574-7583, 2019
222019
Logarithmic regret for reinforcement learning with linear function approximation
J He, D Zhou, Q Gu
International Conference on Machine Learning, 4171-4180, 2021
192021
Stochastic recursive variance-reduced cubic regularization methods
D Zhou, Q Gu
International Conference on Artificial Intelligence and Statistics, 3980-3990, 2020
152020
Neural thompson sampling
W Zhang, D Zhou, L Li, Q Gu
arXiv preprint arXiv:2010.00827, 2020
142020
Provably efficient reinforcement learning with linear function approximation under adaptivity constraints
T Wang, D Zhou, Q Gu
arXiv preprint arXiv:2101.02195, 2021
52021
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation
Y Wu, D Zhou, Q Gu
arXiv preprint arXiv:2102.07301, 2021
42021
Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs
J He, D Zhou, Q Gu
arXiv preprint arXiv:2010.00587, 2020
4*2020
Batched Neural Bandits
Q Gu, A Karbasi, K Khosravi, V Mirrokni, D Zhou
arXiv preprint arXiv:2102.13028, 2021
32021
Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation
Z Chen, D Zhou, Q Gu
arXiv preprint arXiv:2102.07404, 2021
32021
Provably Efficient Representation Learning in Low-rank Markov Decision Processes
W Zhang, J He, D Zhou, A Zhang, Q Gu
arXiv preprint arXiv:2106.11935, 2021
22021
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