Living on the edge: Phase transitions in convex programs with random data D Amelunxen, M Lotz, MB McCoy, JA Tropp Information and Inference: A Journal of the IMA 3 (3), 224-294, 2014 | 619* | 2014 |
Robust computation of linear models by convex relaxation G Lerman, MB McCoy, JA Tropp, T Zhang Foundations of Computational Mathematics 15, 363-410, 2015 | 205* | 2015 |
Two proposals for robust PCA using semidefinite programming M McCoy, JA Tropp | 179 | 2011 |
Sharp recovery bounds for convex demixing, with applications MB McCoy, JA Tropp Foundations of Computational Mathematics 14, 503-567, 2014 | 132 | 2014 |
From Steiner formulas for cones to concentration of intrinsic volumes MB McCoy, JA Tropp Discrete & Computational Geometry 51, 926-963, 2014 | 60 | 2014 |
Convexity in source separation: Models, geometry, and algorithms MB McCoy, V Cevher, QT Dinh, A Asaei, L Baldassarre IEEE Signal Processing Magazine 31 (3), 87-95, 2014 | 57 | 2014 |
The achievable performance of convex demixing MB McCoy, JA Tropp arXiv preprint arXiv:1309.7478, 2013 | 49 | 2013 |
Concentration of the intrinsic volumes of a convex body M Lotz, MB McCoy, I Nourdin, G Peccati, JA Tropp Geometric Aspects of Functional Analysis: Israel Seminar (GAFA) 2017-2019 …, 2020 | 18 | 2020 |
A geometric analysis of convex demixing MB McCoy California Institute of Technology, 2013 | 13 | 2013 |