Dongruo Zhou

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Quanquan GuAssociate Professor of Computer Science, UCLAVerified email at cs.ucla.edu
Yuan CaoThe University of Hong KongVerified email at hku.hk
Jiafan HePhD student, Department of Computer Science, UCLAVerified email at ucla.edu
Pan XuDuke UniversityVerified email at duke.edu
Difan ZouThe University of Hong KongVerified email at cs.hku.hk
Jinghui ChenAssistant Professor of Information Sciences and Technology, Penn State UniversityVerified email at psu.edu
Yiqi TangVerified email at osu.edu
Weitong ZhangPh.D. Candidate, Dept. of Computer Science, UCLAVerified email at ucla.edu
Lihong Li (李力鸿)AmazonVerified email at amazon.com
Ziyan YangRice UniversityVerified email at rice.edu
Zixiang ChenGraduate Student of CS, UCLAVerified email at cs.ucla.edu
Csaba SzepesvariDeepMind & University of AlbertaVerified email at cs.ualberta.ca
Tianhao WangPhD student, Department of Statistics and Data Science, Yale UniversityVerified email at yale.edu
Jinfeng Yi (易津锋)JD AI ResearchVerified email at jd.com
LUYAO YUANStudent of Computer Science, University of California, Los AngelesVerified email at g.ucla.edu
Khashayar KhosraviQuantitative ResearcherVerified email at stanford.edu
Vahab MirrokniGoogle Fellow, VP, Google ResearchVerified email at google.com
Amy ZhangAssistant Professor of Electrical and Computer Engineering at University of Texas at AustinVerified email at austin.utexas.edu
Yue WuPhD student, Department of Computer Science, UCLAVerified email at ucla.edu
Jiahao Chen 陳家豪Owner, Responsible AI LLCVerified email at mit.edu

Dongruo Zhou

UCLA

Verified email at cs.ucla.edu - Homepage

Machine Learning


Title Sort by citations Sort by year Sort by 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, 467-492, 2020	594*	2020
Stochastic nested variance reduction for nonconvex optimization D Zhou, P Xu, Q Gu arXiv preprint arXiv:1806.07811, 2018	187*	2018
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	154	2018
Neural contextual bandits with UCB-based exploration D Zhou, L Li, Q Gu International Conference on Machine Learning, 11492-11502, 2020	152	2020
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	140	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	136	2021
Provably efficient reinforcement learning for discounted mdps with feature mapping D Zhou, J He, Q Gu International Conference on Machine Learning, 12793-12802, 2021	104	2021
Logarithmic regret for reinforcement learning with linear function approximation J He, D Zhou, Q Gu International Conference on Machine Learning, 4171-4180, 2021	67	2021
Stochastic Variance-Reduced Cubic Regularization Methods. D Zhou, P Xu, Q Gu J. Mach. Learn. Res. 20 (134), 1-47, 2019	64*	2019
Neural thompson sampling W Zhang, D Zhou, L Li, Q Gu arXiv preprint arXiv:2010.00827, 2020	59	2020
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	56	2020
Lower bounds for smooth nonconvex finite-sum optimization D Zhou, Q Gu International Conference on Machine Learning, 7574-7583, 2019	40	2019
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	38*	2022
Nearly minimax optimal reinforcement learning for discounted MDPs J He, D Zhou, Q Gu Advances in Neural Information Processing Systems 34, 2021	30*	2021
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	28	2021
Stochastic recursive variance-reduced cubic regularization methods D Zhou, Q Gu International Conference on Artificial Intelligence and Statistics, 3980-3990, 2020	24	2020
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	20	2021
Nearly optimal regret for learning adversarial mdps with linear function approximation J He, D Zhou, Q Gu arXiv e-prints, arXiv: 2102.08940, 2021	19*	2021
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	17	2021
Nearly optimal algorithms for linear contextual bandits with adversarial corruptions J He, D Zhou, T Zhang, Q Gu arXiv preprint arXiv:2205.06811, 2022	13	2022

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