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Bayesian nonparametrics: Dependency and Constraint Modeling

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

Note: TALK via Skype!

In this talk I will briefly overview my research during my PhD, and focus on two works I have done. One is on Bayesian nonparametric dependency modelling with dependent normalized random measures. The construction of these dependency models allows easy manipulating of the dependency structures as well as efficient posterior inference. As a second work, I will talk about how to include additional constraints into Bayesian nonparametric models via the Regularized Bayesian Inference (RegBayes). Specifically, I will introduce the Bayesian max-margin clustering framework derived from RegBayes, which has shown significant improvements over other baselines. Finally future plan will be discussed.

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

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