Introduction to derivative-free optimization AR Conn, K Scheinberg, LN Vicente Society for Industrial and Applied Mathematics, 2009 | 1974 | 2009 |

Efficient SVM training using low-rank kernel representations S Fine, K Scheinberg Journal of Machine Learning Research 2 (Dec), 243-264, 2001 | 784 | 2001 |

SARAH: A novel method for machine learning problems using stochastic recursive gradient LM Nguyen, J Liu, K Scheinberg, M Takáč International Conference on Machine Learning, 2613-2621, 2017 | 415 | 2017 |

Recent progress in unconstrained nonlinear optimization without derivatives AR Conn, K Scheinberg, PL Toint Mathematical programming 79 (1), 397-414, 1997 | 346 | 1997 |

Fast alternating linearization methods for minimizing the sum of two convex functions D Goldfarb, S Ma, K Scheinberg Mathematical Programming 141 (1), 349-382, 2013 | 295 | 2013 |

On the convergence of derivative-free methods for unconstrained optimization AR Conn, K Scheinberg, PL Toint Approximation theory and optimization: tributes to MJD Powell, 83-108, 1997 | 266 | 1997 |

Global convergence of general derivative-free trust-region algorithms to first-and second-order critical points AR Conn, K Scheinberg, LN Vicente SIAM Journal on Optimization 20 (1), 387-415, 2009 | 254 | 2009 |

Sparse inverse covariance selection via alternating linearization methods K Scheinberg, S Ma, D Goldfarb Advances in neural information processing systems 23, 2010 | 230 | 2010 |

Efficient block-coordinate descent algorithms for the group lasso Z Qin, K Scheinberg, D Goldfarb Mathematical Programming Computation 5 (2), 143-169, 2013 | 212 | 2013 |

Geometry of interpolation sets in derivative free optimization AR Conn, K Scheinberg, LN Vicente Mathematical programming 111 (1), 141-172, 2008 | 170 | 2008 |

IBM Research TRECVID-2006 Video Retrieval System. M Campbell, A Haubold, S Ebadollahi, D Joshi, MR Naphade, A Natsev, ... TRECVID, 175-182, 2006 | 157 | 2006 |

A derivative free optimization algorithm in practice A Conn, K Scheinberg, P Toint 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and …, 1998 | 152 | 1998 |

SGD and Hogwild! convergence without the bounded gradients assumption L Nguyen, PH Nguyen, M Dijk, P Richtárik, K Scheinberg, M Takác International Conference on Machine Learning, 3750-3758, 2018 | 151 | 2018 |

Stochastic optimization using a trust-region method and random models R Chen, M Menickelly, K Scheinberg Mathematical Programming 169 (2), 447-487, 2018 | 118 | 2018 |

Global convergence rate analysis of unconstrained optimization methods based on probabilistic models C Cartis, K Scheinberg Mathematical Programming 169 (2), 337-375, 2018 | 116 | 2018 |

An efficient implementation of an active set method for SVMs. K Scheinberg, KP Bennett, E Parrado-Hernández Journal of Machine Learning Research 7 (10), 2006 | 115 | 2006 |

Interior point trajectories in semidefinite programming D Goldfarb, K Scheinberg SIAM Journal on Optimization 8 (4), 871-886, 1998 | 111 | 1998 |

A derivative-free algorithm for least-squares minimization H Zhang, AR Conn, K Scheinberg SIAM Journal on Optimization 20 (6), 3555-3576, 2010 | 108 | 2010 |

Convergence of trust-region methods based on probabilistic models AS Bandeira, K Scheinberg, LN Vicente SIAM Journal on Optimization 24 (3), 1238-1264, 2014 | 106 | 2014 |

Convergence rate analysis of a stochastic trust-region method via supermartingales J Blanchet, C Cartis, M Menickelly, K Scheinberg INFORMS journal on optimization 1 (2), 92-119, 2019 | 100* | 2019 |