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 | 188 | 2013 |

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 | 181 | 2013 |

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 | 174* | 2014 |

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 | 173 | 2014 |

Accelerated methods for nonconvex optimization Y Carmon, JC Duchi, O Hinder, A Sidford SIAM Journal on Optimization 28 (2), 1751-1772, 2018 | 150 | 2018 |

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 | 136 | 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 | 112 | 2015 |

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 | 106 | 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 | 93 | 2017 |

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 | 87* | 2016 |

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 | 81* | 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 | 69 | 2015 |

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 | 62 | 2016 |

Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification P Jain, P Netrapalli, SM Kakade, R Kidambi, A Sidford The Journal of Machine Learning Research 18 (1), 8258-8299, 2017 | 61* | 2017 |

Lower bounds for finding stationary points i Y Carmon, JC Duchi, O Hinder, A Sidford Mathematical Programming, 1-50, 2019 | 58 | 2019 |

Convex until proven guilty: Dimension-free acceleration of gradient descent on non-convex functions Y Carmon, JC Duchi, O Hinder, A Sidford Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 58 | 2017 |

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 | 57 | 2015 |

Accelerating stochastic gradient descent for least squares regression P Jain, SM Kakade, R Kidambi, P Netrapalli, A Sidford arXiv preprint arXiv:1704.08227, 2017 | 55* | 2017 |

Almost-linear-time algorithms for markov chains and new spectral primitives for directed graphs MB Cohen, J Kelner, J Peebles, R Peng, AB Rao, A Sidford, A Vladu Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing …, 2017 | 46 | 2017 |

Efficient algorithms for large-scale generalized eigenvector computation and canonical correlation analysis R Ge, C Jin, P Netrapalli, A Sidford International Conference on Machine Learning, 2741-2750, 2016 | 37 | 2016 |