Sahand N. Negahban, Ph.D.

Affiliations: 
2012 Electrical Engineering & Computer Sciences University of California, Berkeley, Berkeley, CA, United States 
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"Sahand Negahban"
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SNBCP

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Martin Wainwright grad student 2012 UC Berkeley
 (Structured Estimation In High-Dimensions.)
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Publications

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Chen N, Lee DKK, Negahban SN. (2019) Super-resolution estimation of cyclic arrival rates Annals of Statistics. 47: 1754-1775
Elenberg ER, Khanna R, Dimakis AG, et al. (2018) Restricted strong convexity implies weak submodularity Annals of Statistics. 46: 3539-3568
Shaham U, Yamada Y, Negahban S. (2018) Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization Neurocomputing. 307: 195-204
Negahban S, Oh S, Shah D. (2017) Rank Centrality: Ranking from Pairwise Comparisons Operations Research. 65: 266-287
Agarwal A, Negahban SN, Wainwright MJ. (2014) Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions 2014 48th Annual Conference On Information Sciences and Systems, Ciss 2014
Brink H, Richards JW, Poznanski D, et al. (2013) Using machine learning for discovery in synoptic survey imaging data Monthly Notices of the Royal Astronomical Society. 435: 1047-1060
Negahban SN, Ravikumar P, Wainwright MJ, et al. (2012) A unified framework for high-dimensional analysis of m-estimators with decomposable regularizers Statistical Science. 27: 538-557
Agarwal A, Negahban S, Wainwright MJ. (2012) Fast global convergence of gradient methods for high-dimensional statistical recovery Annals of Statistics. 40: 2452-2482
Agarwal A, Negahban S, Wainwright MJ. (2012) Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions Annals of Statistics. 40: 1171-1197
Agarwal A, Negahban S, Wainwright MJ. (2012) FASt global convergence of gradient methods for solving regularized M-estimation 2012 Ieee Statistical Signal Processing Workshop, Ssp 2012. 409-412
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