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Foundations of Nonparametric Bayesian Methods (Part III)

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If you have a question about this talk, please contact Peter Orbanz.

This 3-part tutorial will address a machine learning audience, not assumed to be familiar with measure theory or the theory stochastic processes. The course is intended to provide (1) an overview of what nonparametric Bayesian models exist beyond those already used in machine learning, and (2) a basic understanding of the mathematical construction of ’’process’’ models, both existing ones and new models on a variety of possible domains.

Part III : Construction of new models

Recent works in machine learning consider the construction of models on other domains than the simplex, that is, models which nonparametrically generate objects other than probability distributions (such as the binary matrices generated by the Indian Buffet Process). We discuss how nonparametric Bayesian models can be constructed on arbitrary domains, and what limitations we will have to expect for such constructions.


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

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