Data programming: Creating large training sets, quickly AJ Ratner, CM De Sa, S Wu, D Selsam, C Ré Advances in neural information processing systems, 3567-3575, 2016 | 189 | 2016 |

Incremental knowledge base construction using DeepDive J Shin, S Wu, F Wang, C De Sa, C Zhang, C Ré Proceedings of the VLDB Endowment 8 (11), 1310-1321, 2015 | 169 | 2015 |

Global convergence of stochastic gradient descent for some non-convex matrix problems C De Sa, K Olukotun, C Ré arXiv preprint arXiv:1411.1134, 2014 | 130 | 2014 |

Taming the wild: A unified analysis of hogwild-style algorithms CM De Sa, C Zhang, K Olukotun, C Ré Advances in neural information processing systems, 2674-2682, 2015 | 98 | 2015 |

Understanding and optimizing asynchronous low-precision stochastic gradient descent C De Sa, M Feldman, C Ré, K Olukotun ACM SIGARCH Computer Architecture News 45 (2), 561-574, 2017 | 68 | 2017 |

Representation tradeoffs for hyperbolic embeddings C De Sa, A Gu, C Ré, F Sala Proceedings of machine learning research 80, 4460, 2018 | 53 | 2018 |

High-accuracy low-precision training C De Sa, M Leszczynski, J Zhang, A Marzoev, CR Aberger, K Olukotun, ... arXiv preprint arXiv:1803.03383, 2018 | 43 | 2018 |

Generating configurable hardware from parallel patterns R Prabhakar, D Koeplinger, KJ Brown, HJ Lee, C De Sa, C Kozyrakis, ... ACM SIGARCH Computer Architecture News 44 (2), 651-665, 2016 | 43 | 2016 |

Have abstraction and eat performance, too: Optimized heterogeneous computing with parallel patterns KJ Brown, HJ Lee, T Romp, AK Sujeeth, C De Sa, C Aberger, K Olukotun 2016 IEEE/ACM International Symposium on Code Generation and Optimization …, 2016 | 39 | 2016 |

Deepdive: Declarative knowledge base construction C De Sa, A Ratner, C Ré, J Shin, F Wang, S Wu, C Zhang ACM SIGMOD Record 45 (1), 60-67, 2016 | 37 | 2016 |

Ensuring rapid mixing and low bias for asynchronous Gibbs sampling C De Sa, K Olukotun, C Ré JMLR workshop and conference proceedings 48, 1567, 2016 | 31 | 2016 |

DeepDive: declarative knowledge base construction C Zhang, C Ré, M Cafarella, C De Sa, A Ratner, J Shin, F Wang, S Wu Communications of the ACM 60 (5), 93-102, 2017 | 28 | 2017 |

Parallel SGD: When does averaging help? J Zhang, C De Sa, I Mitliagkas, C Ré arXiv preprint arXiv:1606.07365, 2016 | 26 | 2016 |

Accelerated stochastic power iteration C De Sa, B He, I Mitliagkas, C Ré, P Xu Proceedings of machine learning research 84, 58, 2018 | 21 | 2018 |

Incremental knowledge base construction using DeepDive C Sa, A Ratner, C Ré, J Shin, F Wang, S Wu, C Zhang The VLDB Journal—The International Journal on Very Large Data Bases 26 (1 …, 2017 | 14 | 2017 |

Gaussian quadrature for kernel features T Dao, CM De Sa, C Ré Advances in neural information processing systems, 6107-6117, 2017 | 13 | 2017 |

Socratic learning: Augmenting generative models to incorporate latent subsets in training data P Varma, B He, D Iter, P Xu, R Yu, C De Sa, C Ré arXiv preprint arXiv:1610.08123, 2016 | 13 | 2016 |

Scan order in Gibbs sampling: Models in which it matters and bounds on how much BD He, CM De Sa, I Mitliagkas, C Ré Advances in neural information processing systems, 1-9, 2016 | 13 | 2016 |

Rapidly mixing Gibbs sampling for a class of factor graphs using hierarchy width CM De Sa, C Zhang, K Olukotun, C Ré Advances in neural information processing systems, 3097-3105, 2015 | 12 | 2015 |

Flipper: A systematic approach to debugging training sets P Varma, D Iter, C De Sa, C Ré Proceedings of the 2nd Workshop on Human-in-the-Loop Data Analytics, 5, 2017 | 11 | 2017 |