Solving SDD linear systems in nearly *m*log^{1/2}*n* timeMB Cohen, R Kyng, GL Miller, JW Pachocki, R Peng, AB Rao, SC Xu Proceedings of the forty-sixth annual ACM symposium on Theory of computing …, 2014 | 176 | 2014 |

Solving SDD linear systems in nearly *m*log^{1/2}*n* timeMB Cohen, R Kyng, GL Miller, JW Pachocki, R Peng, AB Rao, SC Xu Proceedings of the forty-sixth annual ACM symposium on Theory of computing …, 2014 | 176 | 2014 |

Approximate gaussian elimination for laplacians-fast, sparse, and simple R Kyng, S Sachdeva 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS …, 2016 | 158 | 2016 |

Sparsified cholesky and multigrid solvers for connection laplacians R Kyng, YT Lee, R Peng, S Sachdeva, DA Spielman Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, 2016 | 117 | 2016 |

Algorithms for Lipschitz learning on graphs R Kyng, A Rao, S Sachdeva, DA Spielman Conference on Learning Theory, 1190-1223, 2015 | 71 | 2015 |

Sampling random spanning trees faster than matrix multiplication D Durfee, R Kyng, J Peebles, AB Rao, S Sachdeva Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing …, 2017 | 65 | 2017 |

Fast, provable algorithms for isotonic regression in all l_p-norms R Kyng, A Rao, S Sachdeva Advances in neural information processing systems 28, 2015 | 51 | 2015 |

Iterative Refinement for *ℓ*_{p}-norm RegressionD Adil, R Kyng, R Peng, S Sachdeva Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete …, 2019 | 47 | 2019 |

A framework for analyzing resparsification algorithms R Kyng, J Pachocki, R Peng, S Sachdeva Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete …, 2017 | 35 | 2017 |

Solving directed Laplacian systems in nearly-linear time through sparse LU factorizations MB Cohen, J Kelner, R Kyng, J Peebles, R Peng, AB Rao, A Sidford 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS …, 2018 | 31 | 2018 |

Flows in almost linear time via adaptive preconditioning R Kyng, R Peng, S Sachdeva, D Wang Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing …, 2019 | 28 | 2019 |

A matrix chernoff bound for strongly rayleigh distributions and spectral sparsifiers from a few random spanning trees R Kyng, Z Song 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS …, 2018 | 18 | 2018 |

Maximum flow and minimum-cost flow in almost-linear time L Chen, R Kyng, YP Liu, R Peng, MP Gutenberg, S Sachdeva arXiv preprint arXiv:2203.00671, 2022 | 17 | 2022 |

Hardness results for structured linear systems R Kyng, P Zhang SIAM Journal on Computing 49 (4), FOCS17-280-FOCS17-349, 2020 | 17 | 2020 |

Preconditioning in expectation MB Cohen, R Kyng, JW Pachocki, R Peng, A Rao arXiv preprint arXiv:1401.6236, 2014 | 17 | 2014 |

Approximate gaussian elimination R Kyng PhD thesis. Yale University,, page, 2017 | 11 | 2017 |

Almost-linear-time Weighted -norm Solvers in Slightly Dense Graphs via Sparsification D Adil, B Bullins, R Kyng, S Sachdeva 48th International Colloquium on Automata, Languages, and Programming (ICALP …, 2021 | 9 | 2021 |

Four deviations suffice for rank 1 matrices R Kyng, K Luh, Z Song Advances in Mathematics 375, 107366, 2020 | 9 | 2020 |

Packing LPs are hard to solve accurately, assuming linear equations are hard R Kyng, D Wang, P Zhang Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete …, 2020 | 9 | 2020 |

Fast, Provable Algorithms for Isotonic Regression in all -norms R Kyng, A Rao, S Sachdeva arXiv preprint arXiv:1507.00710, 2015 | 8 | 2015 |