Cooperative graphical models
Name
NIPS-2016-cooperative-graphical-models-Paper.pdf
Description
Published version
Size
1.51 MB
Format
Adobe PDF
Checksum (MD5)
f9439ea6be2e14447b375102ad98d20d
Author(s)
Jegelka, Stefanie Sabrina
Date Issued
December 2016
Journal
Advances in Neural Information Processing Systems
Publisher
Morgan Kaufmann Publishers
Citation
Djolonga, Josip et al. “Cooperative graphical models.” Advances in Neural Information Processing Systems, 29 ( December 2016 © 2016 The Author(s)
Version
Final published version
Abstract
We study a rich family of distributions that capture variable interactions significantly more expressive than those representable with low-treewidth or pairwise graphical models, or log-supermodular models. We call these cooperative graphical models. Yet, this family retains structure, which we carefully exploit for efficient inference techniques. Our algorithms combine the polyhedral structure of submodular functions in new ways with variational inference methods to obtain both lower and upper bounds on the partition function. While our fully convex upper bound is minimized as an SDP or via tree-reweighted belief propagation, our lower bound is tightened via belief propagation or mean-field algorithms. The resulting algorithms are easy to implement and, as our experiments show, effectively obtain good bounds and marginals for synthetic and real-world examples.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/2016/hash/8f85517967795eeef66c225f7883bdcb-Abstract.html
