Scalable fair clustering A Backurs, P Indyk, K Onak, B Schieber, A Vakilian, T Wagner International Conference on Machine Learning, 405-413, 2019 | 231 | 2019 |

Learning space partitions for nearest neighbor search Y Dong, P Indyk, I Razenshteyn, T Wagner arXiv preprint arXiv:1901.08544, 2019 | 109* | 2019 |

Space and time efficient kernel density estimation in high dimensions A Backurs, P Indyk, T Wagner Advances in neural information processing systems 32, 2019 | 67 | 2019 |

Unveiling transformers with lego: a synthetic reasoning task Y Zhang, A Backurs, S Bubeck, R Eldan, S Gunasekar, T Wagner arXiv preprint arXiv:2206.04301, 2022 | 62* | 2022 |

Scalable nearest neighbor search for optimal transport A Backurs, Y Dong, P Indyk, I Razenshteyn, T Wagner International Conference on machine learning, 497-506, 2020 | 62 | 2020 |

Semi-supervised learning on data streams via temporal label propagation T Wagner, S Guha, S Kasiviswanathan, N Mishra International Conference on Machine Learning, 5095-5104, 2018 | 59 | 2018 |

Sample-optimal low-rank approximation of distance matrices P Indyk, A Vakilian, T Wagner, DP Woodruff Conference on Learning Theory, 1723-1751, 2019 | 31 | 2019 |

Learning-based support estimation in sublinear time T Eden, P Indyk, S Narayanan, R Rubinfeld, S Silwal, T Wagner arXiv preprint arXiv:2106.08396, 2021 | 26 | 2021 |

Triangle and four cycle counting with predictions in graph streams JY Chen, T Eden, P Indyk, H Lin, S Narayanan, R Rubinfeld, S Silwal, ... arXiv preprint arXiv:2203.09572, 2022 | 18 | 2022 |

Exponentially improving the complexity of simulating the Weisfeiler-Lehman test with graph neural networks A Aamand, J Chen, P Indyk, S Narayanan, R Rubinfeld, N Schiefer, ... Advances in Neural Information Processing Systems 35, 27333-27346, 2022 | 16 | 2022 |

Approximate nearest neighbors in limited space P Indyk, T Wagner Conference On Learning Theory, 2012-2036, 2018 | 16 | 2018 |

Near-optimal (euclidean) metric compression P Indyk, T Wagner Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete …, 2017 | 16 | 2017 |

Practical data-dependent metric compression with provable guarantees P Indyk, I Razenshteyn, T Wagner Advances in Neural Information Processing Systems 30, 2017 | 15 | 2017 |

A graph-theoretic approach to multitasking N Alon, D Reichman, I Shinkar, T Wagner, S Musslick, JD Cohen, ... Advances in neural information processing systems 30, 2017 | 15 | 2017 |

Generalization bounds for data-driven numerical linear algebra P Bartlett, P Indyk, T Wagner Conference on Learning Theory, 2013-2040, 2022 | 14 | 2022 |

Towards Resistance Sparsifiers M Dinitz, R Krauthgamer, T Wagner arXiv preprint arXiv:1506.07568, 2015 | 13 | 2015 |

Few-shot data-driven algorithms for low rank approximation P Indyk, T Wagner, D Woodruff Advances in Neural Information Processing Systems 34, 10678-10690, 2021 | 8 | 2021 |

Faster kernel matrix algebra via density estimation A Backurs, P Indyk, C Musco, T Wagner International Conference on Machine Learning, 500-510, 2021 | 8 | 2021 |

Optimal (euclidean) metric compression P Indyk, T Wagner SIAM Journal on Computing 51 (3), 467-491, 2022 | 6 | 2022 |

Fast private kernel density estimation via locality sensitive quantization T Wagner, Y Naamad, N Mishra International Conference on Machine Learning, 35339-35367, 2023 | 5 | 2023 |