Weijie Su

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Emmanuel CandesThe Simons Chair in Mathematics and Statistics, Stanford UniversityVerifisert e-postadresse på stanford.edu
Malgorzata BogdanProfessor of Statistics, University of WroclawVerifisert e-postadresse på uwr.edu.pl
Qi LongProfessor, University of PennsylvaniaVerifisert e-postadresse på upenn.edu
Hangfeng HeUniversity of RochesterVerifisert e-postadresse på rochester.edu
Stephen BoydProfessor of Electrical Engineering, Computer Science, and Management Science, StanfordVerifisert e-postadresse på stanford.edu
Aaron RothProfessor of Computer Science, University of PennsylvaniaVerifisert e-postadresse på cis.upenn.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerifisert e-postadresse på cs.berkeley.edu
Zhun DengAssistant Professor, CS@UNC Chapel HillVerifisert e-postadresse på unc.edu
Cong FangPeking UniversityVerifisert e-postadresse på pku.edu.cn
Linjun ZhangAssociate Professor of Statistics, Rutgers UniversityVerifisert e-postadresse på rutgers.edu
Yangxinyu XieDepartment of Statistics and Data Science at the University of PennsylvaniaVerifisert e-postadresse på wharton.upenn.edu
Tanwi MallickArgonne National LaboratoryVerifisert e-postadresse på anl.gov
Dan RothProfessor of Computer Science, University of PennsylvaniaVerifisert e-postadresse på seas.upenn.edu
Li ZhangMicrosoftVerifisert e-postadresse på microsoft.com
Jason M. KlusowskiAssistant Professor, Department of Operations Research & Financial EngineeringVerifisert e-postadresse på princeton.edu
Cynthia RushColumbia UniversityVerifisert e-postadresse på columbia.edu
Cynthia DworkMicrosoft ResearchVerifisert e-postadresse på microsoft.com
Christopher RéComputer Science, Stanford UniversityVerifisert e-postadresse på cs.stanford.edu
Rina Foygel BarberUniversity of ChicagoVerifisert e-postadresse på uchicago.edu
Jiancong XiaoUniversity of PennsylvaniaVerifisert e-postadresse på upenn.edu

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Weijie Su

Andre navnWeijie J. Su

Associate Professor, University of Pennsylvania

Verifisert e-postadresse på wharton.upenn.edu - Startside

Machine Learning Differential Privacy High-Dimensional Statistics Optimization Deep Learning


Tittel Sorter etter sitater Sorter etter år Sorter etter tittel	Sitert av Sitert av	År
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	1442	2016
Gaussian differential privacy J Dong, A Roth, WJ Su Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2022	575	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	408	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	303	2022
Deep learning with Gaussian differential privacy Z Bu, J Dong, Q Long, WJ Su Harvard Data Science Review 2020 (23), 2020	237	2020
False discoveries occur early on the lasso path W Su, M Bogdan, E Candes The Annals of Statistics 45 (5), 2133-2150, 2017	212	2017
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	204	2021
SLOPE is adaptive to unknown sparsity and asymptotically minimax W Su, E Candes The Annals of Statistics 44 (3), 1038-1068, 2016	160	2016
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	149	2019
Statistical estimation and testing via the sorted L1 norm M Bogdan, E Berg, W Su, E Candes Stanford Statistics Tech Report, 2013	136*	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	90	2022
Federated f-Differential Privacy Q Zheng, S Chen, Q Long, W Su International Conference on Artificial Intelligence and Statistics 130, 2251 …, 2021	75	2021
Higrad: Uncertainty quantification for online learning and stochastic approximation WJ Su, Y Zhu Journal of Machine Learning Research 24 (124), 1-53, 2023	71*	2023
On learning rates and Schrödinger operators B Shi, W Su, MI Jordan Journal of Machine Learning Research 24 (379), 1-53, 2023	67	2023
Familywise error rate control via knockoffs L Janson, W Su	61	2016
Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment R Cummings, D Desfontaines, D Evans, R Geambasu, Y Huang, ... Harvard Data Science Review, 2024	60*	2024
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	59	2020
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	55	2019
Differentially private false discovery rate control C Dwork, WJ Su, L Zhang Journal of Privacy and Confidentiality 11 (2), 2021	52*	2021
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	50*	2023

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