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Restricting exchangeable nonparametric distributions
Sinead Williamson · Steven MacEachern · Eric Xing

Fri Dec 06 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor #None

Distributions over exchangeable matrices with infinitely many columns are useful in constructing nonparametric latent variable models. However, the distribution implied by such models over the number of features exhibited by each data point may be poorly-suited for many modeling tasks. In this paper, we propose a class of exchangeable nonparametric priors obtained by restricting the domain of existing models. Such models allow us to specify the distribution over the number of features per data point, and can achieve better performance on data sets where the number of features is not well-modeled by the original distribution.

Author Information

Sinead Williamson (UT Austin)
Steve MacEachern (The Ohio State University)
Eric Xing (Petuum Inc. / Carnegie Mellon University)

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