Sahand Negahban
Sahand Negahban
Associate Professor, Yale University
E-mailová adresa ověřena na: - Domovská stránka
A Unified Framework for High-Dimensional Analysis of -Estimators with Decomposable Regularizers
SN Negahban, P Ravikumar, MJ Wainwright, B Yu
Estimation of (near) low-rank matrices with noise and high-dimensional scaling
S Negahban, MJ Wainwright
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
Understanding adversarial training: Increasing local stability of supervised models through robust optimization
U Shaham, Y Yamada, S Negahban
Neurocomputing 307, 195-204, 2018
Iterative ranking from pair-wise comparisons
S Negahban, S Oh, D Shah
Advances in neural information processing systems 25, 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
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
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
A Agarwal, S Negahban, MJ Wainwright
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
Restricted strong convexity implies weak submodularity
ER Elenberg, R Khanna, AG Dimakis, S Negahban
The Annals of Statistics 46 (6B), 3539-3568, 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
Feature selection using stochastic gates
Y Yamada, O Lindenbaum, S Negahban, Y Kluger
International conference on machine learning, 10648-10659, 2020
Scalable greedy feature selection via weak submodularity
R Khanna, E Elenberg, A Dimakis, S Negahban, J Ghosh
Artificial Intelligence and Statistics, 1560-1568, 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
Individualized rank aggregation using nuclear norm regularization
Y Lu, SN Negahban
2015 53rd Annual Allerton Conference on Communication, Control, and …, 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
Learning from comparisons and choices
S Negahban, S Oh, KK Thekumparampil, J Xu
Journal of Machine Learning Research 19 (40), 1-95, 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
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
Phase transitions for high-dimensional joint support recovery
S Negahban, MJ Wainwright
Advances in Neural Information Processing Systems 21, 2008
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Články 1–20