Weijie J. Su (苏炜杰)
Weijie J. Su (苏炜杰)
The Wharton School and Computer and Information Science Department, University of Pennsylvania
Verified email at - Homepage
Cited by
Cited by
A differential equation for modeling Nesterov's accelerated gradient method: Theory and insights
W Su, S Boyd, E Candes
Journal of Machine Learning Research 17 (153), 1-43, 2016
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
False discoveries occur early on the lasso path
W Su, M Bogdan, E Candes
The Annals of Statistics 45 (5), 2133-2150, 2017
Gaussian differential privacy
J Dong, A Roth, WJ Su
Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2022
SLOPE is adaptive to unknown sparsity and asymptotically minimax
W Su, E Candes
The Annals of Statistics 44 (3), 1038-1068, 2016
Understanding the acceleration phenomenon via high-resolution differential equations
B Shi, SS Du, MI Jordan, WJ Su
Mathematical Programming, 1-70, 2021
Deep learning with Gaussian differential privacy
Z Bu, J Dong, Q Long, WJ Su
Harvard Data Science Review 2020 (23), 2020
Statistical estimation and testing via the sorted L1 norm
M Bogdan, E Berg, W Su, E Candes
Stanford Statistics Tech Report, 2013
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
Familywise error rate control via knockoffs
L Janson, W Su
Electronic Journal of Statistics 10 (1), 960-975, 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
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
Uncertainty Quantification for Online Learning and Stochastic Approximation via Hierarchical Incremental Gradient Descent
WJ Su, Y Zhu
arXiv preprint arXiv:1802.04876, 2018
Quantitative analysis of the VANET connectivity: Theory and application
X Jin, W Su, W Yan
2011 IEEE 73rd Vehicular Technology Conference (VTC Spring), 1-5, 2011
Differentially private false discovery rate control
C Dwork, WJ Su, L Zhang
Journal of Privacy and Confidentiality 11 (2), 2021
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), 2021
A study of the VANET connectivity by percolation theory
X Jin, W Su, YAN Wei
2011 IEEE Consumer Communications and Networking Conference (CCNC), 85-89, 2011
The local elasticity of neural networks
H He, WJ Su
International Conference on Learning Representations, 2020
A power analysis for knockoffs with the lasso coefficient-difference statistic
A Weinstein, WJ Su, M Bogdan, RF Barber, EJ Candes
arXiv preprint arXiv:2007.15346, 2020
Assumption lean regression
R Berk, A Buja, L Brown, E George, AK Kuchibhotla, W Su, L Zhao
The American Statistician, 1-17, 2019
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