Fred Roosta
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Newton-type methods for non-convex optimization under inexact Hessian information
P Xu, F Roosta, MW Mahoney
Mathematical Programming 184 (1), 35-70, 2020
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
Sub-sampled Newton methods
F Roosta-Khorasani, MW Mahoney
Mathematical Programming 174, 293-326, 2019
Giant: Globally improved approximate newton method for distributed optimization
S Wang, F Roosta, P Xu, MW Mahoney
Advances in Neural Information Processing Systems 31, 2018
Sub-sampled Newton methods with non-uniform sampling
P Xu, J Yang, F Roosta, C Ré, MW Mahoney
Advances in Neural Information Processing Systems 29, 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
Sub-sampled newton methods ii: Local convergence rates
F Roosta-Khorasani, MW Mahoney
arXiv preprint arXiv:1601.04738, 2016
Parallel local graph clustering
J Shun, F Roosta-Khorasani, K Fountoulakis, MW Mahoney
arXiv preprint arXiv:1604.07515, 2016
Inexact Nonconvex Newton-type Methods
Z Yao, P Xu, F Roosta, MW Mahoney
INFORMS Journal on Optimization 3 (2), 154-182, 2021
DINGO: Distributed Newton-type method for gradient-norm optimization
R Crane, F Roosta
Advances in neural information processing systems 32, 2019
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
Newton-MR: Inexact Newton Method with minimum residual sub-problem solver
F Roosta, Y Liu, P Xu, MW Mahoney
EURO Journal on Computational Optimization 10, 100035, 2022
Evolution and application of digital technologies to predict crop type and crop phenology in agriculture
AB Potgieter, Y Zhao, PJ Zarco-Tejada, K Chenu, Y Zhang, K Porker, ...
in silico Plants 3 (1), diab017, 2021
Invariance of weight distributions in rectified MLPs
R Tsuchida, F Roosta, M Gallagher
International Conference on Machine Learning, 4995-5004, 2018
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
Variational perspective on local graph clustering
K Fountoulakis, F Roosta-Khorasani, J Shun, X Cheng, MW Mahoney
Mathematical Programming 174, 553-573, 2019
Optimization methods for inverse problems
N Ye, F Roosta-Khorasani, T Cui
2017 MATRIX Annals, 121-140, 2019
The reproducing Stein kernel approach for post-hoc corrected sampling
L Hodgkinson, R Salomone, F Roosta
arXiv preprint arXiv:2001.09266, 2020
Stochastic normalizing flows
L Hodgkinson, C van der Heide, F Roosta, MW Mahoney
arXiv preprint arXiv:2002.09547, 2020
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
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