| Introduction to derivative-free optimization AR Conn, K Scheinberg, LN Vicente Society for Industrial and Applied Mathematics, 2009 | 2389 | 2009 |
| Efficient SVM training using low-rank kernel representations S Fine, K Scheinberg Journal of Machine Learning Research 2 (Dec), 243-264, 2001 | 860 | 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 | 655 | 2017 |
| Recent progress in unconstrained nonlinear optimization without derivatives AR Conn, K Scheinberg, PL Toint Mathematical programming 79, 397-414, 1997 | 374 | 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 | 323 | 2013 |
| 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 | 290 | 2009 |
| 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 | 274 | 1997 |
| Sparse inverse covariance selection via alternating linearization methods K Scheinberg, S Ma, D Goldfarb Advances in neural information processing systems 23, 2010 | 252 | 2010 |
| Efficient block-coordinate descent algorithms for the group lasso Z Qin, K Scheinberg, D Goldfarb Mathematical Programming Computation 5 (2), 143-169, 2013 | 237 | 2013 |
| 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 | 234 | 2018 |
| Geometry of interpolation sets in derivative free optimization AR Conn, K Scheinberg, LN Vicente Mathematical programming 111, 141-172, 2008 | 191 | 2008 |
| Stochastic optimization using a trust-region method and random models R Chen, M Menickelly, K Scheinberg Mathematical Programming 169, 447-487, 2018 | 183 | 2018 |
| A theoretical and empirical comparison of gradient approximations in derivative-free optimization AS Berahas, L Cao, K Choromanski, K Scheinberg Foundations of Computational Mathematics 22 (2), 507-560, 2022 | 178* | 2022 |
| Global convergence rate analysis of unconstrained optimization methods based on probabilistic models C Cartis, K Scheinberg Mathematical Programming 169, 337-375, 2018 | 170 | 2018 |
| A derivative free optimization algorithm in practice A Conn, K Scheinberg, P Toint 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and …, 1998 | 165 | 1998 |
| 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 | 157* | 2019 |
| 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 stochastic line search method with expected complexity analysis C Paquette, K Scheinberg SIAM Journal on Optimization 30 (1), 349-376, 2020 | 148* | 2020 |
| Convergence of trust-region methods based on probabilistic models AS Bandeira, K Scheinberg, LN Vicente SIAM Journal on Optimization 24 (3), 1238-1264, 2014 | 137 | 2014 |
| 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 | 136 | 2006 |
