A differential equation for modeling Nesterov's accelerated gradient method: theory and insights W Su, S Boyd, EJ Candes Journal of Machine Learning Research 17 (153), 5312-5354, 2016 | 1294 | 2016 |
Gaussian differential privacy J Dong, A Roth, WJ Su Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2022 | 424 | 2022 |
SLOPE—adaptive variable selection via convex optimization M Bogdan, E van den Berg, C Sabatti, W Su, EJ Candès The Annals of Applied Statistics 9 (3), 1103, 2015 | 330 | 2015 |
Understanding the acceleration phenomenon via high-resolution differential equations B Shi, SS Du, MI Jordan, WJ Su Mathematical Programming 195 (1), 79-148, 2022 | 247 | 2022 |
False discoveries occur early on the lasso path W Su, M Bogdan, E Candes The Annals of Statistics 45 (5), 2133-2150, 2017 | 218 | 2017 |
Deep learning with Gaussian differential privacy Z Bu, J Dong, Q Long, WJ Su Harvard Data Science Review 2020 (23), 2020 | 201 | 2020 |
SLOPE is adaptive to unknown sparsity and asymptotically minimax W Su, E Candes The Annals of Statistics 44 (3), 1038-1068, 2016 | 158 | 2016 |
Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training C Fang, H He, Q Long, WJ Su Proceedings of the National Academy of Sciences 118 (43), e2103091118, 2021 | 144* | 2021 |
Acceleration via symplectic discretization of high-resolution differential equations B Shi, SS Du, WJ Su, MI Jordan Advances in Neural Information Processing Systems 32, 5744-5752, 2019 | 126 | 2019 |
Statistical estimation and testing via the sorted L1 norm M Bogdan, E Berg, W Su, E Candes Stanford Statistics Tech Report, 2013 | 94 | 2013 |
An unconstrained layer-peeled perspective on neural collapse W Ji, Y Lu, Y Zhang, Z Deng, WJ Su International Conference on Learning Representations (ICLR), 2022 | 64 | 2022 |
Higrad: Uncertainty quantification for online learning and stochastic approximation WJ Su, Y Zhu Journal of Machine Learning Research 24 (124), 1-53, 2023 | 62* | 2023 |
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing Z Bu, JM Klusowski, C Rush, WJ Su IEEE Transactions on Information Theory 67 (1), 506-537, 2020 | 56 | 2020 |
Familywise error rate control via knockoffs L Janson, W Su | 56 | 2016 |
Group slope–adaptive selection of groups of predictors D Brzyski, A Gossmann, W Su, M Bogdan Journal of the American Statistical Association 114 (525), 419-433, 2019 | 53 | 2019 |
On learning rates and Schrödinger operators B Shi, W Su, MI Jordan Journal of Machine Learning Research 24 (379), 1-53, 2023 | 52 | 2023 |
Federated f-Differential Privacy Q Zheng, S Chen, Q Long, W Su International Conference on Artificial Intelligence and Statistics 130, 2251 …, 2021 | 50 | 2021 |
Differentially private false discovery rate control C Dwork, WJ Su, L Zhang Journal of Privacy and Confidentiality 11 (2), 2021 | 48* | 2021 |
The local elasticity of neural networks H He, WJ Su International Conference on Learning Representations, 2020 | 43 | 2020 |
A power analysis for model-X knockoffs with -regularized statistics A Weinstein, WJ Su, M Bogdan, R Foygel Barber, EJ Candès The Annals of Statistics 51 (3), 1005-1029, 2023 | 40* | 2023 |