2008-07-09-miller08a.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
The phylogenetic Indian Buffet process: a non-exchangeable nonparametric prior for latent features	Nonparametric Bayesian models are often based on the assumption that the objects being modeled are exchangeable. While appropriate in some applications (e.g., bag-of-words models for documents), exchangeability is sometimes assumed simply for computational reasons; non-exchangeable models might be a better choice for applications based on subject matter. Drawing on ideas from graphical models and phylogenetics, we describe a non-exchangeable prior for a class of nonparametric latent feature models that is nearly as efficient computationally as its exchangeable counterpart. Our model is applicable to the general setting in which the dependencies between objects can be expressed using a tree, where edge lengths indicate the strength of relationships. We demonstrate an application to modeling probabilistic choice.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	miller08a	0	The phylogenetic Indian Buffet process: a non-exchangeable nonparametric prior for latent features	403	410	403-410	403	false	McAllester, David A. and Myllym{"a}ki, Petri	given family David A. McAllester given family Petri Myllymäki	Miller, Kurt T. and Griffiths, Thomas L. and Jordan, Michael I.	given family Kurt T. Miller given family Thomas L. Griffiths given family Michael I. Jordan	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/miller08a/miller08a.pdf