NeurIPS 2018spotlight67 citations
Leveraged volume sampling for linear regression
Michal Derezinski, Manfred K. Warmuth, Daniel J. Hsu
Abstract
Suppose an n x d design matrix in a linear regression problem is given, but the response for each point is hidden unless explicitly requested. The goal is to sample only a small number k << n of the responses, and then produce a weight vector whose sum of squares loss over
BibTeX
@inproceedings{NEURIPS2018_2ba8698b,
author = {Derezinski, Michal and Warmuth, Manfred K. K and Hsu, Daniel J},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Leveraged volume sampling for linear regression},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/2ba8698b79439589fdd2b0f7218d8b07-Paper.pdf},
volume = {31},
year = {2018}
}