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 | 205 | 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 | 205 | 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 | 182 | 2016 |

Maximum Flow and Minimum-Cost Flow in Almost-Linear Time S Chen, Li and Kyng, Rasmus and Liu, Yang P. and Peng, Richard and Gutenberg ... 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS), 2022 | 162 | 2022 |

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 | 139 | 2016 |

Algorithms for Lipschitz learning on graphs R Kyng, A Rao, S Sachdeva, DA Spielman Conference on Learning Theory, 1190-1223, 2015 | 87 | 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 | 76 | 2017 |

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 | 65 | 2019 |

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 | 55 | 2015 |

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 | 42 | 2018 |

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 | 39 | 2017 |

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 | 38 | 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 | 24 | 2018 |

Preconditioning in expectation MB Cohen, R Kyng, JW Pachocki, R Peng, A Rao arXiv preprint arXiv:1401.6236, 2014 | 19 | 2014 |

Hardness results for structured linear systems R Kyng, P Zhang SIAM Journal on Computing 49 (4), FOCS17-280-FOCS17-349, 2020 | 18 | 2020 |

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 | 17 | 2021 |

Approximate gaussian elimination R Kyng PhD thesis. Yale University,, page, 2017 | 15 | 2017 |

Four deviations suffice for rank 1 matrices R Kyng, K Luh, Z Song Advances in Mathematics 375, 107366, 2020 | 14 | 2020 |

Fast, Provable Algorithms for Isotonic Regression in all -norms R Kyng, A Rao, S Sachdeva arXiv preprint arXiv:1507.00710, 2015 | 13 | 2015 |

Faster Sparse Matrix Inversion and Rank Computation in Finite Fields S Casacuberta, R Kyng 13th Innovations in Theoretical Computer Science Conference (ITCS 2022), 2021 | 10 | 2021 |