Accelerated Sampling for the Indian Buffet Process


Doshi-Velez F, Ghahramani Z. Accelerated Sampling for the Indian Buffet Process, in Proceedings of the 26th International Conference on Machine Learning. Montreal, Canada ; 2009.
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We often seek to identify co-occurring hidden features in a set of observations. The Indian Buffet Process (IBP) provides a nonparametric prior on the features present in each observation, but current inference techniques for the IBP often scale poorly. The collapsed Gibbs sampler for the IBP has a running time cubic in the number of observations, and the uncollapsed Gibbs sampler, while linear, is often slow to mix. We present a new linear-time collapsed Gibbs sampler for conjugate likelihood models and demonstrate its efficacy on large real-world datasets.

Last updated on 10/11/2018