Matrix completion with noise EJ Candes, Y Plan Proceedings of the IEEE 98 (6), 925-936, 2010 | 2072 | 2010 |

Tight oracle inequalities for low-rank matrix recovery from a minimal number of noisy random measurements EJ Candes, Y Plan IEEE Transactions on Information Theory 57 (4), 2342-2359, 2011 | 716 | 2011 |

A probabilistic and RIPless theory of compressed sensing EJ Candes, Y Plan IEEE transactions on information theory 57 (11), 7235-7254, 2011 | 688 | 2011 |

Near-ideal model selection by *ℓ*_{1} minimizationEJ Candès, Y Plan | 607 | 2009 |

Robust 1-bit compressed sensing and sparse logistic regression: A convex programming approach Y Plan, R Vershynin IEEE Transactions on Information Theory 59 (1), 482-494, 2012 | 495 | 2012 |

One‐bit compressed sensing by linear programming Y Plan, R Vershynin Communications on pure and Applied Mathematics 66 (8), 1275-1297, 2013 | 446 | 2013 |

1-bit matrix completion MA Davenport, Y Plan, E Van Den Berg, M Wootters Information and Inference: A Journal of the IMA 3 (3), 189-223, 2014 | 387 | 2014 |

Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism E Arias-Castro, EJ Candès, Y Plan | 237 | 2011 |

The generalized lasso with non-linear observations Y Plan, R Vershynin IEEE Transactions on information theory 62 (3), 1528-1537, 2016 | 203 | 2016 |

One-bit compressed sensing with non-Gaussian measurements A Ai, A Lapanowski, Y Plan, R Vershynin Linear Algebra and its Applications 441, 222-239, 2014 | 164 | 2014 |

Dimension reduction by random hyperplane tessellations Y Plan, R Vershynin Discrete & Computational Geometry 51 (2), 438-461, 2014 | 145 | 2014 |

High-dimensional estimation with geometric constraints Y Plan, R Vershynin, E Yudovina Information and Inference: A Journal of the IMA 6 (1), 1-40, 2017 | 144 | 2017 |

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 | 128 | 2017 |

Tight analyses for non-smooth stochastic gradient descent NJA Harvey, C Liaw, Y Plan, S Randhawa Conference on Learning Theory, 1579-1613, 2019 | 123 | 2019 |

A simple tool for bounding the deviation of random matrices on geometric sets C Liaw, A Mehrabian, Y Plan, R Vershynin Geometric Aspects of Functional Analysis: Israel Seminar (GAFA) 2014–2016 …, 2017 | 73 | 2017 |

Nearly tight sample complexity bounds for learning mixtures of gaussians via sample compression schemes H Ashtiani, S Ben-David, N Harvey, C Liaw, A Mehrabian, Y Plan Advances in Neural Information Processing Systems 31, 2018 | 61 | 2018 |

Uniqueness conditions for low-rank matrix recovery YC Eldar, D Needell, Y Plan Applied and Computational Harmonic Analysis 33 (2), 309-314, 2012 | 47 | 2012 |

Average-case hardness of RIP certification T Wang, Q Berthet, Y Plan Advances in Neural Information Processing Systems 29, 2016 | 46 | 2016 |

Near-optimal sample complexity bounds for robust learning of gaussian mixtures via compression schemes H Ashtiani, S Ben-David, NJA Harvey, C Liaw, A Mehrabian, Y Plan Journal of the ACM (JACM) 67 (6), 1-42, 2020 | 42 | 2020 |

Tight oracle bounds for low-rank matrix recovery from a minimal number of random measurements EJ Candes, Y Plan arXiv preprint arXiv:1001.0339, 2010 | 39 | 2010 |