| Gradient descent optimizes over-parameterized deep ReLU networks D Zou, Y Cao, D Zhou, Q Gu Machine learning 109, 467-492, 2020 | 716 | 2020 |
| Neural contextual bandits with UCB-based exploration D Zhou, L Li, Q Gu International Conference on Machine Learning, 11492-11502, 2020 | 249 | 2020 |
| Neural thompson sampling W Zhang, D Zhou, L Li, Q Gu arXiv preprint arXiv:2010.00827, 2020 | 235 | 2020 |
| Stochastic nested variance reduction for nonconvex optimization D Zhou, P Xu, Q Gu arXiv preprint arXiv:1806.07811, 2018 | 218* | 2018 |
| Nearly minimax optimal reinforcement learning for linear mixture markov decision processes D Zhou, Q Gu, C Szepesvari Conference on Learning Theory, 4532-4576, 2021 | 217 | 2021 |
| 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 | 198 | 2018 |
| 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 | 183 | 2018 |
| Provably efficient reinforcement learning for discounted mdps with feature mapping D Zhou, J He, Q Gu International Conference on Machine Learning, 12793-12802, 2021 | 136 | 2021 |
| Logarithmic regret for reinforcement learning with linear function approximation J He, D Zhou, Q Gu International Conference on Machine Learning, 4171-4180, 2021 | 99 | 2021 |
| Stochastic variance-reduced cubic regularization methods D Zhou, P Xu, Q Gu Journal of Machine Learning Research 20 (134), 1-47, 2019 | 70* | 2019 |
| 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 | 63 | 2020 |
| Almost optimal algorithms for two-player zero-sum linear mixture markov games Z Chen, D Zhou, Q Gu International Conference on Algorithmic Learning Theory, 227-261, 2022 | 59* | 2022 |
| Lower bounds for smooth nonconvex finite-sum optimization D Zhou, Q Gu International Conference on Machine Learning, 7574-7583, 2019 | 50 | 2019 |
| Nearly minimax optimal reinforcement learning for linear markov decision processes J He, H Zhao, D Zhou, Q Gu International Conference on Machine Learning, 12790-12822, 2023 | 49 | 2023 |
| Provably efficient reinforcement learning with linear function approximation under adaptivity constraints T Wang, D Zhou, Q Gu Advances in Neural Information Processing Systems 34, 13524-13536, 2021 | 46 | 2021 |
| Nearly minimax optimal reinforcement learning for discounted MDPs J He, D Zhou, Q Gu Advances in Neural Information Processing Systems 34, 2021 | 44* | 2021 |
| Nearly optimal algorithms for linear contextual bandits with adversarial corruptions J He, D Zhou, T Zhang, Q Gu Advances in neural information processing systems 35, 34614-34625, 2022 | 40 | 2022 |
| Computationally efficient horizon-free reinforcement learning for linear mixture mdps D Zhou, Q Gu Advances in neural information processing systems 35, 36337-36349, 2022 | 37 | 2022 |
| Variance-aware off-policy evaluation with linear function approximation Y Min, T Wang, D Zhou, Q Gu Advances in neural information processing systems 34, 7598-7610, 2021 | 35 | 2021 |
| Reward-free model-based reinforcement learning with linear function approximation W Zhang, D Zhou, Q Gu Advances in Neural Information Processing Systems 34, 1582-1593, 2021 | 33 | 2021 |
Quanquan GuAssociate Professor of Computer Science, UCLA在 cs.ucla.edu 的电子邮件经过验证
