Outlier detection using nonconvex penalized regression Y She, AB Owen Journal of the American Statistical Association 106 (494), 626-639, 2011 | 369 | 2011 |
Optimal selection of reduced rank estimators of high-dimensional matrices F Bunea, Y She, MH Wegkamp | 273 | 2011 |
Why deep learning works: A manifold disentanglement perspective PP Brahma, D Wu, Y She IEEE transactions on neural networks and learning systems 27 (10), 1997-2008, 2015 | 211 | 2015 |
Sparse regression with exact clustering Y She Stanford University, 2008 | 196 | 2008 |
Thresholding-based iterative selection procedures for model selection and shrinkage Y She | 182 | 2009 |
Penalized least squares regression methods and applications to neuroimaging F Bunea, Y She, H Ombao, A Gongvatana, K Devlin, R Cohen Neuroimage 55 (4), 1519-1527, 2011 | 166 | 2011 |
A comparison of typical ℓp minimization algorithms Q Lyu, Z Lin, Y She, C Zhang Neurocomputing 119, 413-424, 2013 | 146 | 2013 |
Joint variable and rank selection for parsimonious estimation of high-dimensional matrices F Bunea, Y She, MH Wegkamp | 140 | 2012 |
Feature selection with annealing for computer vision and big data learning A Barbu, Y She, L Ding, G Gramajo IEEE transactions on pattern analysis and machine intelligence 39 (2), 272-286, 2016 | 136 | 2016 |
The group square-root lasso: Theoretical properties and fast algorithms F Bunea, J Lederer, Y She IEEE Transactions on Information Theory 60 (2), 1313-1325, 2013 | 117 | 2013 |
An iterative algorithm for fitting nonconvex penalized generalized linear models with grouped predictors Y She Computational Statistics & Data Analysis 56 (10), 2976-2990, 2012 | 109 | 2012 |
Robust reduced-rank regression Y She, K Chen Biometrika 104 (3), 633-647, 2017 | 65 | 2017 |
Group regularized estimation under structural hierarchy Y She, Z Wang, H Jiang Journal of the American Statistical Association 113 (521), 445-454, 2018 | 54 | 2018 |
Selective factor extraction in high dimensions Y She Biometrika 104 (1), 97-110, 2017 | 54* | 2017 |
Robust orthogonal complement principal component analysis Y She, S Li, D Wu Journal of the American Statistical Association 111 (514), 763-771, 2016 | 35 | 2016 |
Group iterative spectrum thresholding for super-resolution sparse spectral selection Y She, J Wang, H Li, D Wu IEEE Transactions on signal Processing 61 (24), 6371-6386, 2013 | 31 | 2013 |
Reduced Rank Vector Generalized Linear Models for Feature Extraction Y She Statistics and Its Interface 6, 197-209, 2013 | 28 | 2013 |
Reinforced robust principal component pursuit PP Brahma, Y She, S Li, J Li, D Wu IEEE transactions on neural networks and learning systems 29 (5), 1525-1538, 2017 | 25 | 2017 |
Stationary-Sparse Causality Network Learning. Y He, Y She, D Wu Journal of Machine Learning Research 14, 2013 | 18 | 2013 |
On the finite-sample analysis of -estimators Y She | 17 | 2016 |