A Unified Framework for High-Dimensional Analysis of SN Negahban, P Ravikumar, MJ Wainwright, B Yu
1638 2012 Estimation of (near) low-rank matrices with noise and high-dimensional scaling S Negahban, MJ Wainwright
687 2011 Restricted strong convexity and weighted matrix completion: Optimal bounds with noise S Negahban, MJ Wainwright
The Journal of Machine Learning Research 13 (1), 1665-1697, 2012
614 2012 Understanding adversarial training: Increasing local stability of supervised models through robust optimization U Shaham, Y Yamada, S Negahban
Neurocomputing 307, 195-204, 2018
565 2018 Iterative ranking from pair-wise comparisons S Negahban, S Oh, D Shah
Advances in neural information processing systems 25, 2012
559 2012 Fast global convergence rates of gradient methods for high-dimensional statistical recovery A Agarwal, S Negahban, MJ Wainwright
Advances in Neural Information Processing Systems 23, 2010
455 2010 Analysis of machine learning techniques for heart failure readmissions BJ Mortazavi, NS Downing, EM Bucholz, K Dharmarajan, A Manhapra, ...
Circulation: Cardiovascular Quality and Outcomes 9 (6), 629-640, 2016
403 2016 Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions A Agarwal, S Negahban, MJ Wainwright
314 2012 Simultaneous Support Recovery in High Dimensions: Benefits and Perils of Block SN Negahban, MJ Wainwright
IEEE Transactions on Information Theory 57 (6), 3841-3863, 2011
222 * 2011 Feature selection using stochastic gates Y Yamada, O Lindenbaum, S Negahban, Y Kluger
International conference on machine learning, 10648-10659, 2020
177 2020 Restricted strong convexity implies weak submodularity ER Elenberg, R Khanna, AG Dimakis, S Negahban
The Annals of Statistics 46 (6B), 3539-3568, 2018
172 2018 Using machine learning for discovery in synoptic survey imaging data H Brink, JW Richards, D Poznanski, JS Bloom, J Rice, S Negahban, ...
Monthly Notices of the Royal Astronomical Society 435 (2), 1047-1060, 2013
159 2013 Scalable greedy feature selection via weak submodularity R Khanna, E Elenberg, A Dimakis, S Negahban, J Ghosh
Artificial Intelligence and Statistics, 1560-1568, 2017
103 2017 Comparison of machine learning methods with national cardiovascular data registry models for prediction of risk of bleeding after percutaneous coronary intervention BJ Mortazavi, EM Bucholz, NR Desai, C Huang, JP Curtis, FA Masoudi, ...
JAMA network open 2 (7), e196835-e196835, 2019
76 2019 Individualized rank aggregation using nuclear norm regularization Y Lu, SN Negahban
2015 53rd Annual Allerton Conference on Communication, Control, and …, 2015
60 2015 Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions A Agarwal, S Negahban, MJ Wainwright
Advances in Neural Information Processing Systems 25, 2012
57 2012 Learning from comparisons and choices S Negahban, S Oh, KK Thekumparampil, J Xu
Journal of Machine Learning Research 19 (40), 1-95, 2018
52 2018 Prediction of adverse events in patients undergoing major cardiovascular procedures BJ Mortazavi, N Desai, J Zhang, A Coppi, F Warner, HM Krumholz, ...
IEEE journal of biomedical and health informatics 21 (6), 1719-1729, 2017
39 2017 Warm-starting contextual bandits: Robustly combining supervised and bandit feedback C Zhang, A Agarwal, H Daumé III, J Langford, SN Negahban
arXiv preprint arXiv:1901.00301, 2019
36 2019 Phase transitions for high-dimensional joint support recovery S Negahban, MJ Wainwright
Advances in Neural Information Processing Systems 21, 2008
26 2008 