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Learning Mixtures of Tree Graphical Models
Anima Anandkumar · Daniel Hsu · Furong Huang · Sham M Kakade

Wed Dec 05 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor #None
We consider unsupervised estimation of mixtures of discrete graphical models, where the class variable is hidden and each mixture component can have a potentially different Markov graph structure and parameters over the observed variables. We propose a novel method for estimating the mixture components with provable guarantees. Our output is a tree-mixture model which serves as a good approximation to the underlying graphical model mixture. The sample and computational requirements for our method scale as $\poly(p, r)$, for an $r$-component mixture of $p$-variate graphical models, for a wide class of models which includes tree mixtures and mixtures over bounded degree graphs.

Author Information

Anima Anandkumar (Caltech)
Daniel Hsu (Columbia University)

See <https://www.cs.columbia.edu/~djhsu/>

Furong Huang (University of Maryland, College Park)
Sham M Kakade (Microsoft Research)

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