Pan Xu

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Quanquan GuAssociate Professor of Computer Science, UCLAEmail yang diverifikasi di cs.ucla.edu
Difan ZouThe University of Hong KongEmail yang diverifikasi di cs.hku.hk
Dongruo ZhouIndiana University BloomingtonEmail yang diverifikasi di iu.edu
Tianyuan JinNational University of SingaporeEmail yang diverifikasi di u.nus.edu
Jinghui ChenAssistant Professor of Information Sciences and Technology, Penn State UniversityEmail yang diverifikasi di psu.edu
Xiaokui XiaoNational University of SingaporeEmail yang diverifikasi di nus.edu.sg
Yue WuPostdoctoral Research Fellow, Princeton UniversityEmail yang diverifikasi di ucla.edu
Weitong ZhangAssistant Professor, SDSS, UNC Chapel HillEmail yang diverifikasi di unc.edu
Anima AnandkumarCalifornia Institute of Technology and NVIDIAEmail yang diverifikasi di caltech.edu
Lingxiao WangToyota Technological Institute at ChicagoEmail yang diverifikasi di ttic.edu
Jian MaRay and Stephanie Lane Professor of Computational Biology, Carnegie Mellon UniversityEmail yang diverifikasi di cs.cmu.edu
Farzad FarnoudUniversity of VirginiaEmail yang diverifikasi di virginia.edu
Kamyar AzizzadenesheliNvidiaEmail yang diverifikasi di nvidia.com
Tianhao WangResearch Assistant Professor, Toyota Technological Institute at ChicagoEmail yang diverifikasi di ttic.edu
Zheng WenGoogle DeepMindEmail yang diverifikasi di google.com
Handong ZhaoAdobe ResearchEmail yang diverifikasi di ece.neu.edu
Doina PrecupDeepMind and McGill UniversityEmail yang diverifikasi di cs.mcgill.ca
A. Rupam MahmoodUniversity of Alberta, AmiiEmail yang diverifikasi di ualberta.ca
Yaodong YuUniversity of California, BerkeleyEmail yang diverifikasi di berkeley.edu
Eric MazumdarAssistant Professor, California Institute of TechnologyEmail yang diverifikasi di caltech.edu

Ikuti

Pan Xu

Duke University

Email yang diverifikasi di duke.edu - Beranda

Machine Learning Optimization Reinforcement Learning High-dimensional Statistics AI


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States EY Cramer, EL Ray, VK Lopez, J Bracher, A Brennen, ... Proceedings of the National Academy of Sciences 119 (15), e2113561119, 2022	330*	2022
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization P Xu, J Chen, D Zou, Q Gu Advances in Neural Information Processing Systems, 3122-3133, 2018	238	2018
Stochastic Nested Variance Reduction for Nonconvex Optimization D Zhou, P Xu, Q Gu Advances in Neural Information Processing Systems, 3921-3932, 2018	235*	2018
Ensemble forecasts of coronavirus disease 2019 (COVID-19) in the US EL Ray, N Wattanachit, J Niemi, AH Kanji, K House, EY Cramer, J Bracher, ... MedRXiv, 2020.08. 19.20177493, 2020	221	2020
A finite-time analysis of two time-scale actor-critic methods YF Wu, W Zhang, P Xu, Q Gu Advances in Neural Information Processing Systems 33, 17617-17628, 2020	166	2020
Epidemic model guided machine learning for COVID-19 forecasts in the United States D Zou, L Wang, P Xu, J Chen, W Zhang, Q Gu MedRxiv, 2020.05. 24.20111989, 2020	126	2020
An improved convergence analysis of stochastic variance-reduced policy gradient P Xu, F Gao, Q Gu Uncertainty in Artificial Intelligence, 541-551, 2020	122	2020
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction P Xu, F Gao, Q Gu International Conference on Learning Representations, 2020	111	2020
The United States COVID-19 Forecast Hub dataset EY Cramer, Y Huang, Y Wang, EL Ray, M Cornell, J Bracher, A Brennen, ... Scientific Data 9 (1), 1-15, 2022	109	2022
A finite-time analysis of Q-learning with neural network function approximation P Xu, Q Gu International Conference on Machine Learning, 10555-10565, 2020	99	2020
Stochastic Variance-Reduced Cubic Regularization Methods D Zhou, P Xu, Q Gu Journal of Machine Learning Research 20 (134), 1-47, 2019	81*	2019
Neural Contextual Bandits with Deep Representation and Shallow Exploration P Xu, Z Wen, H Zhao, Q Gu International Conference on Learning Representations, 2022	74	2022
Faster convergence of stochastic gradient langevin dynamics for non-log-concave sampling D Zou, P Xu, Q Gu Uncertainty in Artificial Intelligence, 1152-1162, 2021	50	2021
Multiple models for outbreak decision support in the face of uncertainty K Shea, RK Borchering, WJM Probert, E Howerton, TL Bogich, SL Li, ... Proceedings of the National Academy of Sciences 120 (18), e2207537120, 2023	49*	2023
Stochastic Variance-Reduced Hamilton Monte Carlo Methods D Zou, P Xu, Q Gu International Conference on Machine Learning, 6028-6037, 2018	42	2018
MOTS: Minimax Optimal Thompson Sampling T Jin, P Xu, J Shi, X Xiao, Q Gu International Conference on Machine Learning, 5074-5083, 2021	40	2021
Langevin Monte Carlo for Contextual Bandits P Xu, H Zheng, EV Mazumdar, K Azizzadenesheli, A Anandkumar International Conference on Machine Learning, 24830-24850, 2022	35	2022
Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics D Zou, P Xu, Q Gu International Conference on Uncertainty in Artificial Intelligence, 2018	32	2018
Global convergence of localized policy iteration in networked multi-agent reinforcement learning Y Zhang, G Qu, P Xu, Y Lin, Z Chen, A Wierman Proceedings of the ACM on Measurement and Analysis of Computing Systems 7 (1 …, 2023	31	2023
Stochastic gradient Hamiltonian monte carlo methods with recursive variance reduction D Zou, P Xu, Q Gu Advances in Neural Information Processing Systems, 3835-3846, 2019	31	2019

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