| 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 | 218 | 2022 |
| 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 |
| Distributed learning without distress: Privacy-preserving empirical risk minimization B Jayaraman, L Wang, D Evans, Q Gu Advances in Neural Information Processing Systems 31, 2018 | 199 | 2018 |
| Learning one-hidden-layer relu networks via gradient descent X Zhang, Y Yu, L Wang, Q Gu The 22nd international conference on artificial intelligence and statistics …, 2019 | 154 | 2019 |
| Revisiting membership inference under realistic assumptions B Jayaraman, L Wang, K Knipmeyer, Q Gu, D Evans arXiv preprint arXiv:2005.10881, 2020 | 151 | 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 |
| 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), 462, 2022 | 99 | 2022 |
| Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US EY Cramer, EL Ray, VK Lopez, J Bracher, A Brennen, ... Medrxiv, 2021.02. 03.21250974, 2021 | 91 | 2021 |
| A unified computational and statistical framework for nonconvex low-rank matrix estimation L Wang, X Zhang, Q Gu arXiv preprint arXiv:1610.05275, 2016 | 87 | 2016 |
| Improving neural language generation with spectrum control L Wang, J Huang, K Huang, Z Hu, G Wang, Q Gu International Conference on Learning Representations, 2019 | 83 | 2019 |
| Is neuron coverage a meaningful measure for testing deep neural networks? F Harel-Canada, L Wang, MA Gulzar, Q Gu, M Kim Proceedings of the 28th ACM Joint Meeting on European Software Engineering …, 2020 | 66 | 2020 |
| Efficient privacy-preserving stochastic nonconvex optimization L Wang, B Jayaraman, D Evans, Q Gu Uncertainty in Artificial Intelligence, 2203-2213, 2023 | 52* | 2023 |
| A unified framework for nonconvex low-rank plus sparse matrix recovery X Zhang, L Wang, Q Gu International Conference on Artificial Intelligence and Statistics, 1097-1107, 2018 | 50* | 2018 |
| A primal-dual analysis of global optimality in nonconvex low-rank matrix recovery X Zhang, L Wang, Y Yu, Q Gu International conference on machine learning, 5862-5871, 2018 | 47 | 2018 |
| Precision matrix estimation in high dimensional gaussian graphical models with faster rates L Wang, X Ren, Q Gu Artificial Intelligence and Statistics, 177-185, 2016 | 35 | 2016 |
| Differentially private iterative gradient hard thresholding for sparse learning L Wang, Q Gu 28th International Joint Conference on Artificial Intelligence, 2019 | 32 | 2019 |
| COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support K Shea, RK Borchering, WJM Probert, E Howerton, TL Bogich, S Li, ... Medrxiv, 2020 | 31 | 2020 |
| DP-LSSGD: A stochastic optimization method to lift the utility in privacy-preserving ERM B Wang, Q Gu, M Boedihardjo, L Wang, F Barekat, SJ Osher Mathematical and Scientific Machine Learning, 328-351, 2020 | 29 | 2020 |
| High-dimensional variance-reduced stochastic gradient expectation-maximization algorithm R Zhu, L Wang, C Zhai, Q Gu International Conference on Machine Learning, 4180-4188, 2017 | 27 | 2017 |
| A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery L Wang, X Zhang, Q Gu International Conference on Machine Learning, 2017, 3712-3721, 2017 | 26* | 2017 |
Quanquan GuAssociate Professor of Computer Science, UCLAVerified email at cs.ucla.edu
