| Improving adversarial robustness requires revisiting misclassified examples Y Wang, D Zou, J Yi, J Bailey, X Ma, Q Gu International conference on learning representations, 2019 | 846 | 2019 |
| Gradient descent optimizes over-parameterized deep ReLU networks D Zou, Y Cao, D Zhou, Q Gu Machine learning 109, 467-492, 2020 | 776 | 2020 |
| Layer-dependent importance sampling for training deep and large graph convolutional networks D Zou, Z Hu, Y Wang, S Jiang, Y Sun, Q Gu Advances in neural information processing systems 32, 2019 | 356 | 2019 |
| 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 | 321* | 2022 |
| An improved analysis of training over-parameterized deep neural networks D Zou, Q Gu NeurIPS 2018, 2019 | 279 | 2019 |
| Global convergence of Langevin dynamics based algorithms for nonconvex optimization P Xu, J Chen, D Zou, Q Gu Advances in Neural Information Processing Systems 31, 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 | 219 | 2020 |
| How much over-parameterization is sufficient to learn deep ReLU networks? Z Chen, Y Cao, D Zou, Q Gu arXiv preprint arXiv:1911.12360, 2019 | 145 | 2019 |
| 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 | 123 | 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 | 107 | 2022 |
| A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave J Bracher, D Wolffram, J Deuschel, K Görgen, JL Ketterer, A Ullrich, ... Nature communications 12 (1), 5173, 2021 | 91* | 2021 |
| Benign overfitting of constant-stepsize sgd for linear regression D Zou, J Wu, V Braverman, Q Gu, S Kakade Conference on Learning Theory, 4633-4635, 2021 | 71 | 2021 |
| A 1Mbps real-time NLOS UV scattering communication system with receiver diversity over 1km G Wang, K Wang, C Gong, D Zou, Z Jiang, Z Xu IEEE Photonics Journal 10 (2), 1-13, 2018 | 65 | 2018 |
| Information security risks outside the laser beam in terrestrial free-space optical communication D Zou, Z Xu IEEE Photonics Journal 8 (5), 1-9, 2016 | 62 | 2016 |
| How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? J Wu, D Zou, Z Chen, V Braverman, Q Gu, PL Bartlett ICLR 2024, 2024 | 57 | 2024 |
| Understanding the generalization of adam in learning neural networks with proper regularization D Zou, Y Cao, Y Li, Q Gu ICLR 2023, 2023 | 53 | 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 | 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 | 48* | 2023 |
| Characterization on practical photon counting receiver in optical scattering communication D Zou, C Gong, K Wang, Z Xu IEEE Transactions on Communications 67 (3), 2203-2217, 2018 | 48 | 2018 |
| Direction matters: On the implicit bias of stochastic gradient descent with moderate learning rate J Wu, D Zou, V Braverman, Q Gu arXiv preprint arXiv:2011.02538, 2020 | 44 | 2020 |
Quanquan GuAssociate Professor of Computer Science, UCLABestätigte E-Mail-Adresse bei cs.ucla.edu
