2008-07-09-welling08a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	note	address	container-title	volume	genre	issued	pdf	extras
Hybrid variational/gibbs collapsed inference in topic models	Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvantages: collapsed Gibbs sampling is unbiased but is also inefficient for large count values and requires averaging over many samples to reduce variance. On the other hand, variational Bayesian inference is efficient and accurate for large count values but suffers from bias for small counts. We propose a hybrid algorithm that combines the best of both worlds: it samples very small counts and applies variational updates to large counts. This hybridization is shown to significantly improve test-set perplexity relative to variational inference at no computational cost.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	welling08a	0	Hybrid variational/gibbs collapsed inference in topic models	587	594	587-594	587	false	McAllester, David A. and Myllym{"a}ki, Petri	given family David A. McAllester given family Petri Myllymäki	Welling, Max and Teh, Yee Whye and Kappen, Bert	given family Max Welling given family Yee Whye Teh given family Bert Kappen	2008-07-09	Reissued by PMLR on 30 October 2024.		Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence	R6	inproceedings	date-parts 2008 7 9	http://proceedings.mlr.press/r6/welling08a/welling08a.pdf