Sahand Negahban
Sahand Negahban
Assistant Professor, Yale University
Verified email at yale.edu
TitleCited byYear
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
9042012
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
3682011
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
3372012
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
2592010
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
2102012
Iterative ranking from pair-wise comparisons
S Negahban, S Oh, D Shah
Advances in neural information processing systems, 2474-2482, 2012
1792012
Understanding adversarial training: Increasing local stability of supervised models through robust optimization
U Shaham, Y Yamada, S Negahban
Neurocomputing 307, 195-204, 2018
1302018
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
932013
Rank centrality: Ranking from pairwise comparisons
S Negahban, S Oh, D Shah
Operations Research 65 (1), 266-287, 2016
882016
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
872011
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
742008
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
622016
Individualized rank aggregation using nuclear norm regularization
Y Lu, SN Negahban
2015 53rd Annual Allerton Conference on Communication, Control, and …, 2015
432015
Scalable greedy feature selection via weak submodularity
R Khanna, E Elenberg, AG Dimakis, S Negahban, J Ghosh
arXiv preprint arXiv:1703.02723, 2017
312017
Restricted strong convexity implies weak submodularity
ER Elenberg, R Khanna, AG Dimakis, S Negahban
arXiv preprint arXiv:1612.00804, 2016
292016
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
262012
Phase transitions for high-dimensional joint support recovery
S Negahban, MJ Wainwright
Advances in Neural Information Processing Systems, 1161-1168, 2009
222009
Restricted strong convexity implies weak submodularity
ER Elenberg, R Khanna, AG Dimakis, S Negahban
The Annals of Statistics 46 (6B), 3539-3568, 2018
172018
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
132017
Learning from comparisons and choices
S Negahban, S Oh, KK Thekumparampil, J Xu
The Journal of Machine Learning Research 19 (1), 1478-1572, 2018
112018
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