Empirical Inference
Conference Paper
2011
Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference
We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method is provably convergent. By marrying convergent EP ideas from (Opper&Winther, 2005) with covariance decoupling techniques (Wipf&Nagarajan, 2008; Nickisch&Seeger, 2009), it runs at least an order of magnitude faster than the most common EP solver.
| Author(s): | Seeger, M. and Nickisch, H. |
| Book Title: | JMLR Workshop and Conference Proceedings Volume 15: AISTATS 2011 |
| Pages: | 652-660 |
| Year: | 2011 |
| Month: | April |
| Day: | 0 |
| Editors: | Gordon, G. , D. Dunson, M. Dudík |
| Publisher: | MIT Press |
| Bibtex Type: | Conference Paper (inproceedings) |
| Address: | Cambridge, MA, USA |
| Event Name: | 14th International Conference on Artificial Intelligence and Statistics |
| Event Place: | Fort Lauderdale, FL, USA |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Links: | |
BibTex
@inproceedings{SeegerN2011,
title = {Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference },
booktitle = {JMLR Workshop and Conference Proceedings Volume 15: AISTATS 2011},
abstract = {We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method is provably convergent. By marrying convergent EP ideas from (Opper&Winther, 2005) with covariance decoupling techniques (Wipf&Nagarajan, 2008; Nickisch&Seeger, 2009), it runs at least an order of magnitude faster than the most common EP solver.
},
pages = {652-660},
editors = {Gordon, G. , D. Dunson, M. Dudík },
publisher = {MIT Press},
address = {Cambridge, MA, USA},
month = apr,
year = {2011},
slug = {seegern2011},
author = {Seeger, M. and Nickisch, H.},
month_numeric = {4}
}