NeurIPS 2018poster15 citations
High Dimensional Linear Regression using Lattice Basis Reduction
Abstract
We consider a high dimensional linear regression problem where the goal is to efficiently recover an unknown vector \beta^* from n noisy linear observations Y=X \beta^
BibTeX
@inproceedings{NEURIPS2018_ccc0aa1b,
author = {Zadik, Ilias and Gamarnik, David},
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 = {High Dimensional Linear Regression using Lattice Basis Reduction},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/ccc0aa1b81bf81e16c676ddb977c5881-Paper.pdf},
volume = {31},
year = {2018}
}