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Poster
Gibbs-type Indian Buffet Processes
Creighton Heaukulani · Daniel Roy

Thu Dec 11 11:00 AM -- 03:00 PM (PST) @ Level 2, room 210D #None
in Poster Session »

We investigate a class of feature allocation models that generalize the Indian buffet process and are parameterized by Gibbs-type random measures. Two existing classes are contained as special cases: the original two-parameter Indian buffet process, corresponding to the Dirichlet process, and the stable (or three-parameter) Indian buffet process, corresponding to the Pitman--Yor process. Asymptotic behavior of the Gibbs-type partitions, such as power-laws holding for the number of latent clusters, translates into analogous characteristics for this class of Gibbs-type feature allocation models. We derive a number of properties of these models, present a black-box inference procedure, and elucidate the subtle differences between this superclass and its better known subclasses.

Author Information

Koa Heaukulani (Unaffiliated)
Dan Roy (Univ of Toronto & Vector)

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