@inproceedings{b19443daecd5473dbb23dd3cbd9fde38,
title = "Efficient neighborhood selection for walk summable Gaussian graphical models",
abstract = "This paper addresses the problem of learning Gaussian graphical models using a threshold-based greedy neighborhood selection and pruning algorithm. The algorithm leverages the fact that the maximum conditional covariance between a node and its undiscovered neighbors given any estimated neighborhood is always bounded away from zero. We provide theoretical guarantees for the efficiency and accuracy of our algorithm for the class of walk summable Gaussian graphical models. We verify the performance of the algorithm through simulations.",
author = "Yingxang Yang and Jalal Etesami and Negar Kiyavash",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 ; Conference date: 29-10-2017 Through 01-11-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ACSSC.2017.8335180",
language = "English (US)",
series = "Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "263--267",
editor = "Matthews, {Michael B.}",
booktitle = "Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017",
address = "United States",
}