69 citations · 76 across the 2 of their papers we have counts for
8 papers
GPU Accelerated Sub-Sampled Newton's Method
Sudhir B. Kylasa, Farbod Roosta-Khorasani, Michael W. Mahoney +1
First order methods, which solely rely on gradient information, are commonly used in diverse machine learning (ML) and data analysis (DA) applications. This is attributed to the si…
Inexact Non-Convex Newton-Type Methods
Zhewei Yao, Peng Xu, Farbod Roosta-Khorasani +1
For solving large-scale non-convex problems, we propose inexact variants of trust region and adaptive cubic regularization methods, which, to increase efficiency, incorporate vario…
Out-of-sample extension of graph adjacency spectral embedding
Keith Levin, Farbod Roosta-Khorasani, Michael W. Mahoney +1
Many popular dimensionality reduction procedures have out-of-sample extensions, which allow a practitioner to apply a learned embedding to observations not seen in the initial trai…
Optimization Methods for Inverse Problems
Nan Ye, Farbod Roosta-Khorasani, Tiangang Cui
Optimization plays an important role in solving many inverse problems. Indeed, the task of inversion often either involves or is fully cast as a solution of an optimization problem…
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction
Kristofer E. Bouchard, Alejandro F. Bujan, Farbod Roosta-Khorasani +7
The increasing size and complexity of scientific data could dramatically enhance discovery and prediction for basic scientific applications. Realizing this potential, however, requ…
Sub-sampled Newton Methods with Non-uniform Sampling
Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani +2
We consider the problem of finding the minimizer of a convex function of the form where a low-rank facto…