| 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 | 299* | 2022 |
| Stochastic Nested Variance Reduction for Nonconvex Optimization D Zhou, P Xu, Q Gu Advances in Neural Information Processing Systems, 3921-3932, 2018 | 222* | 2018 |
| 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 | 216 | 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 | 215 | 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 | 148 | 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 | 122 | 2020 |
| An improved convergence analysis of stochastic variance-reduced policy gradient P Xu, F Gao, Q Gu Uncertainty in Artificial Intelligence, 541-551, 2020 | 111 | 2020 |
| Sample Efficient Policy Gradient Methods with Recursive Variance Reduction P Xu, F Gao, Q Gu International Conference on Learning Representations, 2020 | 102 | 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 | 99 | 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 | 92 | 2020 |
| Stochastic Variance-Reduced Cubic Regularization Methods D Zhou, P Xu, Q Gu Journal of Machine Learning Research 20 (134), 1-47, 2019 | 73* | 2019 |
| Neural Contextual Bandits with Deep Representation and Shallow Exploration P Xu, Z Wen, H Zhao, Q Gu International Conference on Learning Representations, 2022 | 65 | 2022 |
| 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 | 47* | 2023 |
| 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 | 42 | 2021 |
| Stochastic Variance-Reduced Hamilton Monte Carlo Methods D Zou, P Xu, Q Gu International Conference on Machine Learning, 6028-6037, 2018 | 40 | 2018 |
| MOTS: Minimax Optimal Thompson Sampling T Jin, P Xu, J Shi, X Xiao, Q Gu International Conference on Machine Learning, 5074-5083, 2021 | 38 | 2021 |
| Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics D Zou, P Xu, Q Gu International Conference on Uncertainty in Artificial Intelligence, 2018 | 31 | 2018 |
| Langevin Monte Carlo for Contextual Bandits P Xu, H Zheng, EV Mazumdar, K Azizzadenesheli, A Anandkumar International Conference on Machine Learning, 24830-24850, 2022 | 29 | 2022 |
| 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 | 29 | 2019 |
| Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics D Zou, P Xu, Q Gu The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 26 | 2019 |
Quanquan GuAssociate Professor of Computer Science, UCLA在 cs.ucla.edu 的电子邮件经过验证
