Tensor decompositions for learning latent variable models A Anandkumar, R Ge, D Hsu, SM Kakade, M Telgarsky Journal of machine learning research 15, 2773-2832, 2014 | 1083 | 2014 |

Escaping from saddle points—online stochastic gradient for tensor decomposition R Ge, F Huang, C Jin, Y Yuan Conference on learning theory, 797-842, 2015 | 951 | 2015 |

How to escape saddle points efficiently C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan International Conference on Machine Learning, 1724-1732, 2017 | 631 | 2017 |

Matrix completion has no spurious local minimum R Ge, JD Lee, T Ma Advances in neural information processing systems 29, 2016 | 592 | 2016 |

Generalization and equilibrium in generative adversarial nets (gans) S Arora, R Ge, Y Liang, T Ma, Y Zhang International Conference on Machine Learning, 224-232, 2017 | 556 | 2017 |

A practical algorithm for topic modeling with provable guarantees S Arora, R Ge, Y Halpern, D Mimno, A Moitra, D Sontag, Y Wu, M Zhu International conference on machine learning, 280-288, 2013 | 458 | 2013 |

Learning topic models--going beyond SVD S Arora, R Ge, A Moitra 2012 IEEE 53rd annual symposium on foundations of computer science, 1-10, 2012 | 458 | 2012 |

Computing a nonnegative matrix factorization---provably S Arora, R Ge, R Kannan, A Moitra SIAM Journal on Computing 45 (4), 1582-1611, 2016 | 446 | 2016 |

Stronger generalization bounds for deep nets via a compression approach S Arora, R Ge, B Neyshabur, Y Zhang International Conference on Machine Learning, 254-263, 2018 | 439 | 2018 |

Provable bounds for learning some deep representations S Arora, A Bhaskara, R Ge, T Ma International conference on machine learning, 584-592, 2014 | 383 | 2014 |

No spurious local minima in nonconvex low rank problems: A unified geometric analysis R Ge, C Jin, Y Zheng International Conference on Machine Learning, 1233-1242, 2017 | 366 | 2017 |

Global convergence of policy gradient methods for the linear quadratic regulator M Fazel, R Ge, S Kakade, M Mesbahi International Conference on Machine Learning, 1467-1476, 2018 | 358* | 2018 |

New algorithms for learning in presence of errors S Arora, R Ge Automata, Languages and Programming, 403-415, 2011 | 281 | 2011 |

A tensor spectral approach to learning mixed membership community models A Anandkumar, R Ge, D Hsu, S Kakade Conference on Learning Theory, 867-881, 2013 | 259 | 2013 |

Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma arXiv preprint arXiv:1711.00501, 2017 | 222 | 2017 |

New algorithms for learning incoherent and overcomplete dictionaries S Arora, R Ge, A Moitra Conference on Learning Theory, 779-806, 2014 | 209 | 2014 |

Computational complexity and information asymmetry in financial products S Arora, B Barak, M Brunnermeier, R Ge ICS, 49-65, 2010 | 196 | 2010 |

Simple, efficient, and neural algorithms for sparse coding S Arora, R Ge, T Ma, A Moitra Conference on learning theory, 113-149, 2015 | 183 | 2015 |

Efficient approaches for escaping higher order saddle points in non-convex optimization A Anandkumar, R Ge Conference on learning theory, 81-102, 2016 | 144 | 2016 |

Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization R Frostig, R Ge, S Kakade, A Sidford International Conference on Machine Learning, 2540-2548, 2015 | 139 | 2015 |