| 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 | 951 | 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 | 384 | 2011 |
| Restricted strong convexity and weighted matrix completion: Optimal bounds with noise S Negahban, MJ Wainwright Journal of Machine Learning Research 13 (May), 1665-1697, 2012 | 363 | 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, 37-45, 2010 | 279 | 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 | 221 | 2012 |
| Iterative ranking from pair-wise comparisons S Negahban, S Oh, D Shah Advances in neural information processing systems, 2474-2482, 2012 | 187 | 2012 |
| Understanding adversarial training: Increasing local stability of supervised models through robust optimization U Shaham, Y Yamada, S Negahban Neurocomputing 307, 195-204, 2018 | 148 | 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 | 100 | 2013 |
| Rank centrality: Ranking from pairwise comparisons S Negahban, S Oh, D Shah Operations Research 65 (1), 266-287, 2016 | 98 | 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 | 89 | 2011 |
| 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 | 78 | 2016 |
| Joint support recovery under high-dimensional scaling: Benefits and perils of ℓ 1,∞-regularization S Negahban, MJ Wainwright Proceedings of the 21st International Conference on Neural Information …, 2008 | 76 | 2008 |
| Individualized rank aggregation using nuclear norm regularization Y Lu, SN Negahban 2015 53rd Annual Allerton Conference on Communication, Control, and …, 2015 | 45 | 2015 |
| Scalable greedy feature selection via weak submodularity R Khanna, E Elenberg, AG Dimakis, S Negahban, J Ghosh arXiv preprint arXiv:1703.02723, 2017 | 33 | 2017 |
| Restricted strong convexity implies weak submodularity ER Elenberg, R Khanna, AG Dimakis, S Negahban arXiv preprint arXiv:1612.00804, 2016 | 29 | 2016 |
| Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions A Agarwal, S Negahban, MJ Wainwright Advances in Neural Information Processing Systems, 1538-1546, 2012 | 26 | 2012 |
| Restricted strong convexity implies weak submodularity ER Elenberg, R Khanna, AG Dimakis, S Negahban The Annals of Statistics 46 (6B), 3539-3568, 2018 | 23 | 2018 |
| Phase transitions for high-dimensional joint support recovery S Negahban, MJ Wainwright Advances in Neural Information Processing Systems, 1161-1168, 2009 | 23 | 2009 |
| On approximation guarantees for greedy low rank optimization R Khanna, ER Elenberg, AG Dimakis, J Ghosh, S Negahban Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 15 | 2017 |
| Learning from comparisons and choices S Negahban, S Oh, KK Thekumparampil, J Xu The Journal of Machine Learning Research 19 (1), 1478-1572, 2018 | 13 | 2018 |
