@inproceedings{210c9fc7cd8044de993cd737cdf28974,
title = "A nearly-linear time framework for graph-structured sparsity",
abstract = "We introduce a framework for sparsity structures defined via graphs. Our approach is flexible and generalizes several previously studied sparsity models. Moreover, we provide efficient projection algorithms for our sparsity model that run in nearly-linear time. In the context of sparse recovery, we show that our framework achieves an information-theoretically optimal sample complexity for a wide range of parameters. We complement our theoretical analysis with experiments demonstrating that our algorithms also improve on prior work in practice.",
author = "Chinmay Hegde and Piotr Indyk and Ludwig Schmidt",
note = "Publisher Copyright: Copyright {\textcopyright} 2015 by the author(s).; 32nd International Conference on Machine Learning, ICML 2015 ; Conference date: 06-07-2015 Through 11-07-2015",
year = "2015",
language = "English (US)",
series = "32nd International Conference on Machine Learning, ICML 2015",
publisher = "International Machine Learning Society (IMLS)",
pages = "928--937",
editor = "David Blei and Francis Bach",
booktitle = "32nd International Conference on Machine Learning, ICML 2015",
}