Hanna M. Wallach
An Alternative Prior Process for Nonparametric Bayesian Clustering.
Mon, 10/18/2010 - 15:01 — SomNambulAuthors: Shane T. Jensen, Hanna M. Wallach, Lee Dicker, Katherine A. Heller
Subjects: Methodology
AbstractPrior distributions play a crucial role in Bayesian approaches to clustering.
Two commonly-used prior distributions are the Dirichlet and Pitman-Yor
processes. In this paper, we investigate the predictive probabilities that
underlie these processes, and the implicit "rich-get-richer" characteristic of
the resulting partitions. We explore an alternative prior for nonparametric
Bayesian clustering -- the uniform process -- for applications where the
"rich-get-richer" property is undesirable.Learning the Structure of Deep Sparse Graphical Models.
Tue, 01/05/2010 - 16:01 — SomNambulAuthors: Ryan Prescott Adams, Zoubin Ghahramani, Hanna M. Wallach
Subjects: Machine Learning
AbstractDeep belief networks are a powerful way to model complex probability
distributions. However, learning the structure of a belief network,
particularly one with hidden units, is difficult. The Indian buffet process has
been used as a nonparametric Bayesian prior on the directed structure of a
belief network with a single infinitely wide hidden layer. In this paper, we
introduce the cascading Indian buffet process (CIBP), which provides a
nonparametric prior on the structure of a layered, directed belief network that
is unbounded in both depth and width, yet allows tractable inference.
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