Random matrix methods for wireless communications R Couillet, M Debbah Cambridge University Press, 2011 | 1061 | 2011 |

Large system analysis of linear precoding in correlated MISO broadcast channels under limited feedback S Wagner, R Couillet, M Debbah, DTM Slock IEEE transactions on information theory 58 (7), 4509-4537, 2012 | 732 | 2012 |

A deterministic equivalent for the analysis of correlated MIMO multiple access channels R Couillet, M Debbah, JW Silverstein IEEE Transactions on Information Theory 57 (6), 3493-3514, 2011 | 209 | 2011 |

Electrical vehicles in the smart grid: A mean field game analysis R Couillet, SM Perlaza, H Tembine, M Debbah IEEE Journal on Selected Areas in Communications 30 (6), 1086-1096, 2012 | 201 | 2012 |

A random matrix approach to neural networks C Louart, Z Liao, R Couillet The Annals of Applied Probability 28 (2), 1190-1248, 2018 | 199 | 2018 |

A random matrix analysis of random fourier features: beyond the gaussian kernel, a precise phase transition, and the corresponding double descent Z Liao, R Couillet, MW Mahoney Advances in Neural Information Processing Systems 33, 13939-13950, 2020 | 115 | 2020 |

Large dimensional analysis and optimization of robust shrinkage covariance matrix estimators R Couillet, M McKay Journal of Multivariate Analysis 131, 99-120, 2014 | 115 | 2014 |

Kernel spectral clustering of large dimensional data R Couillet, F Benaych-Georges | 113 | 2016 |

Large system analysis of linear precoding in MISO broadcast channels with confidential messages G Geraci, R Couillet, J Yuan, M Debbah, IB Collings IEEE Journal on Selected Areas in Communications 31 (9), 1660-1671, 2013 | 105 | 2013 |

The random matrix regime of Maronna’s M-estimator with elliptically distributed samples R Couillet, F Pascal, JW Silverstein Journal of Multivariate Analysis 139, 56-78, 2015 | 88 | 2015 |

Robust M-estimation for Array Processing: a random matrix approach R Couillet, F Pascal, JW Silverstein IEEE Trans. Signal Process, 2012 | 82* | 2012 |

Random matrix theory proves that deep learning representations of gan-data behave as gaussian mixtures MEA Seddik, C Louart, M Tamaazousti, R Couillet International Conference on Machine Learning, 8573-8582, 2020 | 76 | 2020 |

Random matrix methods for machine learning R Couillet, Z Liao Cambridge University Press, 2022 | 75 | 2022 |

High-dimensional MVDR beamforming: Optimized solutions based on spiked random matrix models L Yang, MR McKay, R Couillet IEEE Transactions on Signal Processing 66 (7), 1933-1947, 2018 | 70 | 2018 |

A robust statistics approach to minimum variance portfolio optimization L Yang, R Couillet, MR McKay IEEE Transactions on Signal Processing 63 (24), 6684-6697, 2015 | 68 | 2015 |

Signal processing in large systems: A new paradigm R Couillet, M Debbah IEEE Signal Processing Magazine 30 (1), 24-39, 2012 | 67 | 2012 |

On the spectrum of random features maps of high dimensional data Z Liao, R Couillet International Conference on Machine Learning, 3063-3071, 2018 | 66 | 2018 |

Eigen-inference for energy estimation of multiple sources R Couillet, JW Silverstein, Z Bai, M Debbah IEEE Transactions on Information Theory 57 (4), 2420-2439, 2011 | 65 | 2011 |

A large scale analysis of logistic regression: Asymptotic performance and new insights X Mai, Z Liao, R Couillet ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 64 | 2019 |

Fluctuations of spiked random matrix models and failure diagnosis in sensor networks R Couillet, W Hachem IEEE Transactions on Information Theory 59 (1), 509-525, 2012 | 62 | 2012 |