NIPS Proceedingsβ

Restricting exchangeable nonparametric distributions

Part of: Advances in Neural Information Processing Systems 26 (NIPS 2013)

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Conference Event Type: Spotlight

Abstract

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.