A Unified Framework for High-Dimensional Analysis of -Estimators with Decomposable Regularizers SN Negahban, P Ravikumar, MJ Wainwright, B Yu | 1624 | 2012 |
Estimation of (near) low-rank matrices with noise and high-dimensional scaling S Negahban, MJ Wainwright | 679 | 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 | 605 | 2012 |
Understanding adversarial training: Increasing local stability of supervised models through robust optimization U Shaham, Y Yamada, S Negahban Neurocomputing 307, 195-204, 2018 | 555 | 2018 |
Iterative ranking from pair-wise comparisons S Negahban, S Oh, D Shah Advances in neural information processing systems 25, 2012 | 545 | 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 | 444 | 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 | 389 | 2016 |
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions A Agarwal, S Negahban, MJ Wainwright | 312 | 2012 |
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 | 222* | 2011 |
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 |
Feature selection using stochastic gates Y Yamada, O Lindenbaum, S Negahban, Y Kluger International conference on machine learning, 10648-10659, 2020 | 171 | 2020 |
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 | 155 | 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 | 102 | 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 | 74 | 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 | 54 | 2012 |
Learning from comparisons and choices S Negahban, S Oh, KK Thekumparampil, J Xu Journal of Machine Learning Research 19 (40), 1-95, 2018 | 51 | 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 | 38 | 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 | 34 | 2019 |
Phase transitions for high-dimensional joint support recovery S Negahban, MJ Wainwright Advances in Neural Information Processing Systems 21, 2008 | 26 | 2008 |