| Phase retrieval via Wirtinger flow: Theory and algorithms EJ Candes, X Li, M Soltanolkotabi IEEE Transactions on Information Theory 61 (4), 1985-2007, 2015 | 1561 | 2015 |
| Discussion of "Latent Variable Graphical Model Selection via Convex Optimization" EJCM Soltanolkotabi Annals of Statistics 40 (2), 1997-2004, 2012 | 608* | 2012 |
| A geometric analysis of subspace clustering with outliers M Soltanolkotabi, EJ Candes | 495 | 2012 |
| Theoretical insights into the optimization landscape of over-parameterized shallow neural networks M Soltanolkotabi, A Javanmard, JD Lee arXiv preprint arXiv:1707.04926, 2018 | 476 | 2018 |
| Phase Retrieval from Coded Diffraction Patterns E Candes, X Li, M Soltanolkotabi Applied and Computational Harmonic Analysis, 2013 | 470 | 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 | 441 | 2016 |
| 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 | 429 | 2019 |
| Robust subspace clustering M Soltanolkotabi, E Elhamifar, EJ Candes | 425 | 2014 |
| 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 | 414 | 2020 |
| A field guide to federated optimization J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ... arXiv preprint arXiv:2107.06917, 2021 | 381 | 2021 |
| 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 | 369 | 2020 |
| 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 | 363 | 2015 |
| 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 | 214 | 2018 |
| Overparameterized nonlinear learning: Gradient descent takes the shortest path? S Oymak, M Soltanolkotabi International Conference on Machine Learning, 4951-4960, 2019 | 212 | 2019 |
| Learning relus via gradient descent M Soltanolkotabi Advances in neural information processing systems 30, 2017 | 198 | 2017 |
| 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, 1-45, 2012 | 157 | 2012 |
| Convergence and sample complexity of gradient methods for the model-free linear–quadratic regulator problem H Mohammadi, A Zare, M Soltanolkotabi, MR Jovanović IEEE Transactions on Automatic Control 67 (5), 2435-2450, 2021 | 148 | 2021 |
| Gradient methods for submodular maximization H Hassani, M Soltanolkotabi, A Karbasi Advances in Neural Information Processing Systems 30, 2017 | 147 | 2017 |
| Neural networks can learn representations with gradient descent A Damian, J Lee, M Soltanolkotabi Conference on Learning Theory, 5413-5452, 2022 | 125 | 2022 |
| Structured signal recovery from quadratic measurements: Breaking sample complexity barriers via nonconvex optimization M Soltanolkotabi arXiv preprint arXiv:1702.06175, 2018 | 125 | 2018 |
Samet OymakEECS Prof @ University of MichiganAdresse e-mail validée de umich.edu
