| Phase retrieval via Wirtinger flow: Theory and algorithms EJ Candes, X Li, M Soltanolkotabi IEEE Transactions on Information Theory 61 (4), 1985-2007, 2015 | 940 | 2015 |
| Discussion of "Latent Variable Graphical Model Selection via Convex Optimization" EJCM Soltanolkotabi Annals of Statistics 40 (2), 1997-2004, 2012 | 473* | 2012 |
| A geometric analysis of subspace clustering with outliers M Soltanolkotabi, EJ Candes The Annals of Statistics 40 (4), 2195-2238, 2012 | 415 | 2012 |
| Robust subspace clustering M Soltanolkotabi, E Elhamifar, EJ Candes Annals of Statistics 42 (2), 669-699, 2014 | 351 | 2014 |
| Phase Retrieval from Coded Diffraction Patterns E Candes, X Li, M Soltanolkotabi Applied and Computational Harmonic Analysis, 2013 | 320 | 2013 |
| Low-rank solutions of linear matrix equations via procrustes flow S Tu, R Boczar, M Soltanolkotabi, B Recht Proceedings of International Conference on Machine Learning, 2016 | 283 | 2016 |
| Theoretical insights into the optimization landscape of over-parameterized shallow neural networks M Soltanolkotabi, A Javanmard, JD Lee arXiv preprint arXiv:1707.04926, 2018 | 227 | 2018 |
| Experimental robustness of Fourier ptychography phase retrieval algorithms LH Yeh, J Dong, J Zhong, L Tian, M Chen, G Tang, M Soltanolkotabi, ... Optics express 23 (26), 33214-33240, 2015 | 177 | 2015 |
| A unified approach to sparse signal processing F Marvasti, A Amini, F Haddadi, M Soltanolkotabi, BH Khalaj, A Aldroubi, ... EURASIP journal on advances in signal processing 2012 (1), 1-45, 2012 | 130 | 2012 |
| Lagrange coded computing: Optimal design for resiliency, security, and privacy Q Yu, S Li, N Raviv, SMM Kalan, M Soltanolkotabi, SA Avestimehr The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 122 | 2019 |
| Learning relus via gradient descent M Soltanolkotabi arXiv preprint arXiv:1705.04591, 2017 | 103 | 2017 |
| Toward Moderate Overparameterization: Global Convergence Guarantees for Training Shallow Neural Networks S Oymak, M Soltanolkotabi IEEE Journal on Selected Areas in Information Theory 1 (1), 84-105, 2020 | 101 | 2020 |
| Sharp Time--Data Tradeoffs for Linear Inverse Problems S Oymak, B Recht, M Soltanolkotabi arXiv preprint arXiv:1507.04793, 2017 | 86 | 2017 |
| Structured signal recovery from quadratic measurements: Breaking sample complexity barriers via nonconvex optimization M Soltanolkotabi arXiv preprint arXiv:1702.06175, 2018 | 76 | 2018 |
| Compressed sensing with deep image prior and learned regularization D Van Veen, A Jalal, M Soltanolkotabi, E Price, S Vishwanath, ... arXiv preprint arXiv:1806.06438, 2018 | 72 | 2018 |
| Overparameterized nonlinear learning: Gradient descent takes the shortest path? S Oymak, M Soltanolkotabi International Conference on Machine Learning, 4951-4960, 2019 | 66 | 2019 |
| Super-resolution radar R Heckel, VI Morgenshtern, M Soltanolkotabi http://imaiai.oxfordjournals.org/content/early/2016/02/23/imaiai.iaw001, 2016 | 66 | 2016 |
| Gradient methods for submodular maximization H Hassani, M Soltanolkotabi, A Karbasi arXiv preprint arXiv:1708.03949, 2017 | 65 | 2017 |
| Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks M Li, M Soltanolkotabi, S Oymak International Conference on Artificial Intelligence and Statistics, 4313-4324, 2020 | 62 | 2020 |
| Near-Optimal Straggler Mitigation for Distributed Gradient Methods S Li, SMM Kalan, AS Avestimehr, M Soltanolkotabi arXiv preprint arXiv:1710.09990, 2018 | 56 | 2018 |
Emmanuel CandesThe Simons Chair in Mathematics and Statistics, Stanford UniversityVerified email at stanford.edu
