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Accelerated Gibbs Sampling for the Indian Buffet Process

Accelerated Gibbs Sampling for the Indian Buffet Process

This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. 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.

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