Indian Buffet Processes with Power-law Behaviour

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• Yee Whye Teh (Gatsby Unit, UCL)
• Friday 20 November 2009, 14:00-15:00
• Engineering Department, CBL Room 438.

If you have a question about this talk, please contact Zoubin Ghahramani.

The Indian buffet process (IBP) is an exchangeable distribution over binary matrices used in Bayesian nonparametric featural models. In this paper we propose a three-parameter generalization of the IBP exhibiting power-law behavior. We achieve this by generalizing the beta process (the de Finetti measure of the IBP ) to the stable-beta process and deriving the IBP corresponding to it. We find interesting relationships between the stable-beta process and the Pitman-Yor process (another stochastic process used in Bayesian nonparametric models with interesting power-law properties). We derive a stick-breaking construction for the stable-beta process, and find that our power-law IBP is a good model for word occurrences in document corpora.

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

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