A Unified Framework for High-Dimensional Analysis of SN Negahban, P Ravikumar, MJ Wainwright, B Yu
1427 2012 Estimation of (near) low-rank matrices with noise and high-dimensional scaling S Negahban, MJ Wainwright
603 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
550 2012 Understanding adversarial training: Increasing local stability of supervised models through robust optimization U Shaham, Y Yamada, S Negahban
Neurocomputing 307, 195-204, 2018
459 2018 Iterative ranking from pair-wise comparisons S Negahban, S Oh, D Shah
Advances in neural information processing systems 25, 2012
458 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
406 2010 Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions A Agarwal, S Negahban, MJ Wainwright
294 2012 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
277 2016 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
216 * 2011 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
138 2013 Restricted strong convexity implies weak submodularity ER Elenberg, R Khanna, AG Dimakis, S Negahban
The Annals of Statistics 46 (6B), 3539-3568, 2018
132 2018 Scalable greedy feature selection via weak submodularity R Khanna, E Elenberg, A Dimakis, S Negahban, J Ghosh
Artificial Intelligence and Statistics, 1560-1568, 2017
78 2017 Feature selection using stochastic gates Y Yamada, O Lindenbaum, S Negahban, Y Kluger
International Conference on Machine Learning, 10648-10659, 2020
77 2020 Individualized rank aggregation using nuclear norm regularization Y Lu, SN Negahban
2015 53rd Annual Allerton Conference on Communication, Control, and …, 2015
58 2015 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
50 2019 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
46 2012 Learning from comparisons and choices S Negahban, S Oh, KK Thekumparampil, J Xu
The Journal of Machine Learning Research 19 (1), 1478-1572, 2018
45 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
31 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
27 2019 Phase transitions for high-dimensional joint support recovery S Negahban, MJ Wainwright
Advances in Neural Information Processing Systems 21, 2008
24 2008 