Sahand Negahban
Sahand Negahban
Associate Professor, Yale University
Verified email at - Homepage
Cited by
Cited by
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
Estimation of (near) low-rank matrices with noise and high-dimensional scaling
S Negahban, MJ Wainwright
The Annals of Statistics, 1069-1097, 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
Fast global convergence of gradient methods for high-dimensional statistical recovery
A Agarwal, S Negahban, MJ Wainwright
The Annals of Statistics, 2452-2482, 2012
Understanding adversarial training: Increasing local stability of supervised models through robust optimization
U Shaham, Y Yamada, S Negahban
Neurocomputing 307, 195-204, 2018
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
A Agarwal, S Negahban, MJ Wainwright
The Annals of Statistics, 1171-1197, 2012
Iterative ranking from pair-wise comparisons
S Negahban, S Oh, D Shah
Advances in neural information processing systems 25, 2474-2482, 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
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
Rank centrality: Ranking from pairwise comparisons
S Negahban, S Oh, D Shah
Operations Research 65 (1), 266-287, 2017
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
Restricted strong convexity implies weak submodularity
ER Elenberg, R Khanna, AG Dimakis, S Negahban
The Annals of Statistics 46 (6B), 3539-3568, 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
Individualized rank aggregation using nuclear norm regularization
Y Lu, SN Negahban
2015 53rd Annual Allerton Conference on Communication, Control, and …, 2015
Learning from comparisons and choices
S Negahban, S Oh, KK Thekumparampil, J Xu
The Journal of Machine Learning Research 19 (1), 1478-1572, 2018
Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions
A Agarwal, S Negahban, MJ Wainwright
Advances in Neural Information Processing Systems 25, 1538-1546, 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
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
Phase transitions for high-dimensional joint support recovery
S Negahban, MJ Wainwright
Advances in Neural Information Processing Systems 21, 2008
On approximation guarantees for greedy low rank optimization
R Khanna, ER Elenberg, AG Dimakis, J Ghosh, S Negahban
International Conference on Machine Learning, 1837-1846, 2017
The system can't perform the operation now. Try again later.
Articles 1–20