Path finding methods for linear programming: Solving linear programs in o (vrank) iterations and faster algorithms for maximum flow YT Lee, A Sidford 2014 IEEE 55th Annual Symposium on Foundations of Computer Science, 424-433, 2014 | 327* | 2014 |

Accelerated methods for nonconvex optimization Y Carmon, JC Duchi, O Hinder, A Sidford SIAM Journal on Optimization 28 (2), 1751-1772, 2018 | 268 | 2018 |

An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations JA Kelner, YT Lee, L Orecchia, A Sidford Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete …, 2014 | 264 | 2014 |

Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems YT Lee, A Sidford 2013 ieee 54th annual symposium on foundations of computer science, 147-156, 2013 | 261 | 2013 |

A simple, combinatorial algorithm for solving SDD systems in nearly-linear time JA Kelner, L Orecchia, A Sidford, ZA Zhu Proceedings of the forty-fifth annual ACM symposium on Theory of computing …, 2013 | 260 | 2013 |

A faster cutting plane method and its implications for combinatorial and convex optimization YT Lee, A Sidford, SC Wong 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 1049-1065, 2015 | 244 | 2015 |

Uniform sampling for matrix approximation MB Cohen, YT Lee, C Musco, C Musco, R Peng, A Sidford Proceedings of the 2015 Conference on Innovations in Theoretical Computer …, 2015 | 198 | 2015 |

Lower bounds for finding stationary points I Y Carmon, JC Duchi, O Hinder, A Sidford Mathematical Programming 184 (1), 71-120, 2020 | 171 | 2020 |

Near-optimal time and sample complexities for solving Markov decision processes with a generative model A Sidford, M Wang, X Wu, L Yang, Y Ye Advances in Neural Information Processing Systems 31, 2018 | 168* | 2018 |

Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization R Frostig, R Ge, S Kakade, A Sidford International Conference on Machine Learning, 2540-2548, 2015 | 148 | 2015 |

Single pass spectral sparsification in dynamic streams M Kapralov, YT Lee, CN Musco, CP Musco, A Sidford SIAM Journal on Computing 46 (1), 456-477, 2017 | 147 | 2017 |

Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification P Jain, S Kakade, R Kidambi, P Netrapalli, A Sidford Journal of Machine Learning Research 18, 2018 | 142* | 2018 |

Geometric median in nearly linear time MB Cohen, YT Lee, G Miller, J Pachocki, A Sidford Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, 2016 | 136 | 2016 |

Accelerating stochastic gradient descent for least squares regression P Jain, SM Kakade, R Kidambi, P Netrapalli, A Sidford Conference On Learning Theory, 545-604, 2018 | 129* | 2018 |

Efficient inverse maintenance and faster algorithms for linear programming YT Lee, A Sidford 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 230-249, 2015 | 127 | 2015 |

Streaming pca: Matching matrix bernstein and near-optimal finite sample guarantees for oja’s algorithm P Jain, C Jin, SM Kakade, P Netrapalli, A Sidford Conference on learning theory, 1147-1164, 2016 | 124 | 2016 |

Competing with the empirical risk minimizer in a single pass R Frostig, R Ge, SM Kakade, A Sidford Conference on learning theory, 728-763, 2015 | 115 | 2015 |

Robust shift-and-invert preconditioning: Faster and more sample efficient algorithms for eigenvector computation D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford ICML, 2016 | 110* | 2016 |

“Convex Until Proven Guilty”: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions Y Carmon, JC Duchi, O Hinder, A Sidford International conference on machine learning, 654-663, 2017 | 107 | 2017 |

Variance reduced value iteration and faster algorithms for solving markov decision processes A Sidford, M Wang, X Wu, Y Ye Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete …, 2018 | 105 | 2018 |