| Newton-type methods for non-convex optimization under inexact Hessian information P Xu, F Roosta, MW Mahoney Mathematical Programming, 1-36, 2019 | 101 | 2019 |
| Sub-sampled Newton methods II: Local convergence rates F Roosta-Khorasani, MW Mahoney arXiv preprint arXiv:1601.04738, 2016 | 99 | 2016 |
| Sub-sampled Newton methods with non-uniform sampling P Xu, J Yang, F Roosta, C Ré, MW Mahoney Advances in Neural Information Processing Systems, 3000-3008, 2016 | 75 | 2016 |
| Improved bounds on sample size for implicit matrix trace estimators F Roosta-Khorasani, U Ascher Foundations of Computational Mathematics 15 (5), 1187-1212, 2015 | 74 | 2015 |
| Second-order optimization for non-convex machine learning: An empirical study P Xu, F Roosta, MW Mahoney Proceedings of the 2020 SIAM International Conference on Data Mining, 199-207, 2020 | 66 | 2020 |
| Sub-sampled Newton methods F Roosta-Khorasani, MW Mahoney Mathematical Programming 174 (1-2), 293-326, 2019 | 38 | 2019 |
| GIANT: Globally improved approximate newton method for distributed optimization S Wang, F Roosta-Khorasani, P Xu, MW Mahoney Advances in Neural Information Processing Systems, 2338-2348, 2018 | 37 | 2018 |
| Stochastic algorithms for inverse problems involving PDEs and many measurements F Roosta-Khorasani, K van den Doel, U Ascher SIAM Journal on Scientific Computing 36 (5), S3-S22, 2014 | 37 | 2014 |
| Parallel local graph clustering J Shun, F Roosta-Khorasani, K Fountoulakis, MW Mahoney arXiv preprint arXiv:1604.07515, 2016 | 35 | 2016 |
| Inexact non-convex Newton-type methods Z Yao, P Xu, F Roosta-Khorasani, MW Mahoney arXiv preprint arXiv:1802.06925, 2018 | 26 | 2018 |
| Variational perspective on local graph clustering K Fountoulakis, F Roosta-Khorasani, J Shun, X Cheng, MW Mahoney Mathematical Programming, 1-21, 2016 | 20* | 2016 |
| Data completion and stochastic algorithms for PDE inversion problems with many measurements F Roosta-Khorasani, K Doel, U Ascher arXiv preprint arXiv:1312.0707, 2013 | 20 | 2013 |
| Assessing stochastic algorithms for large scale nonlinear least squares problems using extremal probabilities of linear combinations of gamma random variables F Roosta-Khorasani, GJ Székely, UM Ascher SIAM/ASA Journal on Uncertainty Quantification 3 (1), 61-90, 2015 | 16 | 2015 |
| Newton-MR: Newton's Method Without Smoothness or Convexity F Roosta, Y Liu, P Xu, MW Mahoney arXiv preprint arXiv:1810.00303, 2018 | 12 | 2018 |
| Invariance of weight distributions in rectified MLPs R Tsuchida, F Roosta, M Gallagher International Conference on Machine Learning, 4995-5004, 2018 | 12 | 2018 |
| Union of intersections (uoi) for interpretable data driven discovery and prediction K Bouchard, A Bujan, F Roosta, S Ubaru, M Prabhat, A Snijders, JH Mao, ... Advances in Neural Information Processing Systems, 1078-1086, 2017 | 11 | 2017 |
| GPU accelerated sub-sampled newton's method for convex classification problems S Kylasa, F Roosta, MW Mahoney, A Grama Proceedings of the 2019 SIAM International Conference on Data Mining, 702-710, 2019 | 8* | 2019 |
| Randomized algorithms for solving large scale nonlinear least squares problems F Roosta-Khorasani University of British Columbia, 2015 | 8 | 2015 |
| DINGO: Distributed newton-type method for gradient-norm optimization R Crane, F Roosta Advances in Neural Information Processing Systems, 9498-9508, 2019 | 7 | 2019 |
| The reproducing Stein kernel approach for post-hoc corrected sampling L Hodgkinson, R Salomone, F Roosta arXiv preprint arXiv:2001.09266, 2020 | 5 | 2020 |
Michael MahoneyProfessor of Statistics, UC BerkeleyVerified email at stat.berkeley.edu
