Settling the polynomial learnability of mixtures of gaussians A Moitra, G Valiant 2010 IEEE 51st Annual Symposium on Foundations of Computer Science, 93-102, 2010 | 395 | 2010 |

Making ai forget you: Data deletion in machine learning A Ginart, M Guan, G Valiant, JY Zou Advances in neural information processing systems 32, 2019 | 377 | 2019 |

Estimating the unseen: an n/log (n)-sample estimator for entropy and support size, shown optimal via new CLTs G Valiant, P Valiant Proceedings of the forty-third annual ACM symposium on Theory of computing …, 2011 | 348 | 2011 |

Learning from untrusted data M Charikar, J Steinhardt, G Valiant Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing …, 2017 | 326 | 2017 |

What can transformers learn in-context? a case study of simple function classes S Garg, D Tsipras, PS Liang, G Valiant Advances in Neural Information Processing Systems 35, 30583-30598, 2022 | 289 | 2022 |

Efficiently learning mixtures of two Gaussians AT Kalai, A Moitra, G Valiant Proceedings of the forty-second ACM symposium on Theory of computing, 553-562, 2010 | 262 | 2010 |

Optimal algorithms for testing closeness of discrete distributions SO Chan, I Diakonikolas, P Valiant, G Valiant Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete …, 2014 | 244 | 2014 |

An automatic inequality prover and instance optimal identity testing G Valiant, P Valiant SIAM Journal on Computing 46 (1), 429-455, 2017 | 236 | 2017 |

Estimating the unseen: improved estimators for entropy and other properties G Valiant, P Valiant Journal of the ACM (JACM) 64 (6), 1-41, 2017 | 199 | 2017 |

Learning polynomials with neural networks A Andoni, R Panigrahy, G Valiant, L Zhang International conference on machine learning, 1908-1916, 2014 | 199 | 2014 |

Designing network protocols for good equilibria HL Chen, T Roughgarden, G Valiant SIAM Journal on Computing 39 (5), 1799-1832, 2010 | 175 | 2010 |

The power of linear estimators G Valiant, P Valiant 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science, 403-412, 2011 | 170 | 2011 |

Implicit regularization for deep neural networks driven by an ornstein-uhlenbeck like process G Blanc, N Gupta, G Valiant, P Valiant Conference on learning theory, 483-513, 2020 | 158 | 2020 |

Resilience: A criterion for learning in the presence of arbitrary outliers J Steinhardt, M Charikar, G Valiant arXiv preprint arXiv:1703.04940, 2017 | 144 | 2017 |

Finding correlations in subquadratic time, with applications to learning parities and juntas G Valiant 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science, 11-20, 2012 | 110 | 2012 |

Braess's paradox in large random graphs G Valiant, T Roughgarden Proceedings of the 7th ACM conference on Electronic commerce, 296-305, 2006 | 105 | 2006 |

A CLT and tight lower bounds for estimating entropy G Valiant, P Valiant Electronic Colloquium on Computational Complexity (ECCC) 17 (179), 9, 2010 | 101 | 2010 |

Memory, communication, and statistical queries J Steinhardt, G Valiant, S Wager Conference on Learning Theory, 1490-1516, 2016 | 100 | 2016 |

Finding correlations in subquadratic time, with applications to learning parities and the closest pair problem G Valiant Journal of the ACM (JACM) 62 (2), 1-45, 2015 | 93 | 2015 |

Equivariant transformer networks KS Tai, P Bailis, G Valiant International Conference on Machine Learning, 6086-6095, 2019 | 84 | 2019 |