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Bayesian Nonparametrics: Latent Feature and Prediction Models, and Efficient Inference

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Nonparametric Bayesian approaches offer a flexible modeling paradigm for data without limiting the model-complexity a priori. The flexibility comes from the fact that the model-complexity can grow adaptively with data. The Indian Buffet Process (IBP) is an example of a nonparametric Bayesian model in which a set of observations are assumed to be generated from a small set of latent features, and the number of latent features need not be known a priori. In this talk, I will describe some of my recent work on the IBP based models; in particular, (1) A variant of the IBP which removes the independent latent features assumption, and allows the latent features to be related via a hierarchy, (2) A nonparametric Bayesian multitask learning model which uses a combination of the Dirichlet Process mixture model and the IBP as the prior distribution on the weight vectors of multiple tasks, and (3) An efficient, search-based inference method for finding an approximate MAP estimate of the latent feature assignment matrix in the IBP based models.

This talk is part of the Machine Learning @ CUED series.

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