Fred (Farbod) Roosta
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Newton-type methods for non-convex optimization under inexact Hessian information
P Xu, F Roosta, MW Mahoney
Mathematical Programming 187 (1), 35-70, 2020
Sub-sampled Newton methods II: Local convergence rates
F Roosta-Khorasani, MW Mahoney
arXiv preprint arXiv:1601.04738, 2016
Sub-sampled Newton methods with non-uniform sampling
P Xu, J Yang, F Roosta-Khorasani, C Ré, MW Mahoney
arXiv preprint arXiv:1607.00559, 2016
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
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
F Roosta-Khorasani, MW Mahoney
Mathematical Programming 174 (1-2), 293-326, 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
Parallel local graph clustering
J Shun, F Roosta-Khorasani, K Fountoulakis, MW Mahoney
arXiv preprint arXiv:1604.07515, 2016
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
Inexact nonconvex Newton-type methods
Z Yao, P Xu, F Roosta, MW Mahoney
Informs Journal on Optimization, ijoo. 2019.0043, 2021
Variational perspective on local graph clustering
K Fountoulakis, F Roosta-Khorasani, J Shun, X Cheng, MW Mahoney
Mathematical Programming, 1-21, 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
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
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
Newton-MR: Newton's Method Without Smoothness or Convexity
F Roosta, Y Liu, P Xu, MW Mahoney
arXiv preprint arXiv:1810.00303, 2018
Invariance of weight distributions in rectified MLPs
R Tsuchida, F Roosta, M Gallagher
International Conference on Machine Learning, 4995-5004, 2018
Union of intersections (uoi) for interpretable data driven discovery and prediction
KE Bouchard, AF Bujan, F Roosta-Khorasani, S Ubaru, AM Snijders, ...
arXiv preprint arXiv:1705.07585, 2017
Dingo: Distributed Newton-type method for gradient-norm optimization
R Crane, F Roosta
arXiv preprint arXiv:1901.05134, 2019
The reproducing Stein kernel approach for post-hoc corrected sampling
L Hodgkinson, R Salomone, F Roosta
arXiv preprint arXiv:2001.09266, 2020
Optimization methods for inverse problems
N Ye, F Roosta-Khorasani, T Cui
2017 MATRIX Annals, 121-140, 2019
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