| CoSaMP: Iterative signal recovery from incomplete and inaccurate samples D Needell, JA Tropp Applied and computational harmonic analysis 26 (3), 301-321, 2009 | 4777 | 2009 |
| Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit D Needell, R Vershynin Foundations of computational mathematics 9 (3), 317-334, 2009 | 1172 | 2009 |
| Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit D Needell, R Vershynin IEEE Journal of selected topics in signal processing 4 (2), 310-316, 2010 | 1063 | 2010 |
| Compressed sensing with coherent and redundant dictionaries EJ Candes, YC Eldar, D Needell, P Randall Applied and Computational Harmonic Analysis 31 (1), 59-73, 2011 | 893 | 2011 |
| Stochastic gradient descent, weighted sampling, and the randomized kaczmarz algorithm D Needell, N Srebro, R Ward arXiv preprint arXiv:1310.5715, 2013 | 279 | 2013 |
| Stable image reconstruction using total variation minimization D Needell, R Ward SIAM Journal on Imaging Sciences 6 (2), 1035-1058, 2013 | 223 | 2013 |
| Randomized Kaczmarz solver for noisy linear systems D Needell BIT Numerical Mathematics 50 (2), 395-403, 2010 | 186 | 2010 |
| Proving program termination B Cook, A Podelski, A Rybalchenko Communications of the ACM 54 (5), 88-98, 2011 | 179* | 2011 |
| Paved with good intentions: analysis of a randomized block Kaczmarz method D Needell, JA Tropp Linear Algebra and its Applications 441, 199-221, 2014 | 153 | 2014 |
| Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm D Needell, N Srebro, R Ward Mathematical Programming 155 (1-2), 549-573, 2016 | 135 | 2016 |
| Signal space CoSaMP for sparse recovery with redundant dictionaries MA Davenport, D Needell, MB Wakin IEEE Transactions on Information Theory 59 (10), 6820-6829, 2013 | 118 | 2013 |
| Greedy signal recovery review D Needell, J Tropp, R Vershynin 2008 42nd Asilomar conference on signals, systems and computers, 1048-1050, 2008 | 113 | 2008 |
| Acceleration of randomized Kaczmarz method via the Johnson–Lindenstrauss lemma YC Eldar, D Needell Numerical Algorithms 58 (2), 163-177, 2011 | 101 | 2011 |
| Exponential decay of reconstruction error from binary measurements of sparse signals RG Baraniuk, S Foucart, D Needell, Y Plan, M Wootters IEEE Transactions on Information Theory 63 (6), 3368-3385, 2017 | 100 | 2017 |
| CoSaMP: Iterative signal recovery from incomplete and inaccurate samples JA Tropp, D Needell | 96 | 2008 |
| Noisy signal recovery via iterative reweighted l1-minimization D Needell 2009 Conference Record of the Forty-Third Asilomar Conference on Signals …, 2009 | 95 | 2009 |
| Topics in compressed sensing D Needell arXiv preprint arXiv:0905.4482, 2009 | 95 | 2009 |
| Convergence properties of the randomized extended gauss--seidel and Kaczmarz methods A Ma, D Needell, A Ramdas SIAM Journal on Matrix Analysis and Applications 36 (4), 1590-1604, 2015 | 89 | 2015 |
| Linear convergence of stochastic iterative greedy algorithms with sparse constraints N Nguyen, D Needell, T Woolf IEEE Transactions on Information Theory 63 (11), 6869-6895, 2017 | 73 | 2017 |
| We need a research data census F Berman Communications of the ACM 53 (12), 39-41, 2010 | 72* | 2010 |
Joel A. TroppSteele Family Professor of Applied & Computational Mathematics, CaltechVerified email at caltech.edu
