| A Unified Framework for High-Dimensional Analysis of -Estimators with Decomposable Regularizers SN Negahban, P Ravikumar, MJ Wainwright, B Yu Statistical science 27 (4), 538-557, 2012 | 1306 | 2012 |
| Estimation of (near) low-rank matrices with noise and high-dimensional scaling S Negahban, MJ Wainwright The Annals of Statistics 39 (2), 1069-1097, 2011 | 539 | 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 | 490 | 2012 |
| Iterative ranking from pair-wise comparisons S Negahban, S Oh, D Shah Advances in neural information processing systems 25, 2012 | 420 | 2012 |
| Understanding adversarial training: Increasing local stability of supervised models through robust optimization U Shaham, Y Yamada, S Negahban Neurocomputing 307, 195-204, 2018 | 397 | 2018 |
| 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 | 370 | 2010 |
| Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions A Agarwal, S Negahban, MJ Wainwright The Annals of Statistics 40 (2), 1171-1197, 2012 | 274 | 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 | 229 | 2016 |
| Simultaneous Support Recovery in High Dimensions: Benefits and Perils of Block-Regularization SN Negahban, MJ Wainwright IEEE Transactions on Information Theory 57 (6), 3841-3863, 2011 | 206* | 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 | 127 | 2013 |
| Restricted strong convexity implies weak submodularity ER Elenberg, R Khanna, AG Dimakis, S Negahban The Annals of Statistics 46 (6B), 3539-3568, 2018 | 105 | 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 | 69 | 2017 |
| Individualized rank aggregation using nuclear norm regularization Y Lu, SN Negahban 2015 53rd Annual Allerton Conference on Communication, Control, and …, 2015 | 55 | 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 | 43 | 2012 |
| 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 | 42 | 2019 |
| Feature selection using stochastic gates Y Yamada, O Lindenbaum, S Negahban, Y Kluger International Conference on Machine Learning, 10648-10659, 2020 | 41 | 2020 |
| Learning from comparisons and choices S Negahban, S Oh, KK Thekumparampil, J Xu The Journal of Machine Learning Research 19 (1), 1478-1572, 2018 | 41 | 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 |
| Phase transitions for high-dimensional joint support recovery S Negahban, MJ Wainwright Advances in Neural Information Processing Systems 21, 2008 | 23 | 2008 |
| 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 | 21 | 2019 |
