University of Michigan, Ann Arbor
Bayesian nonparametric multilevel clustering with
We present a Bayesian nonparametric framework for multilevel clustering which utilizes group-level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters. Using the Dirichlet process as a building block, our model constructs a product base-measure
with a nested structure to accommodate content and context observations at multiple levels. The proposed model possesses properties that link the nested Dirichlet processes (nDP) and the Dirichlet process mixture models (DPM) in an interesting way: integrating out all contents results in the DPM over contexts,
whereas integrating out group-specific contexts results in the nDP mixture over content variables. We shall present experiments on real-world datasets which demonstrate the advantage of utilizing context information via our model in both text and image data domains. Next, we will discuss our attempts in scaling
up the inference procedure to data sets of millions of data instances, by utilizing the stochastic variational method and a parallel computing architecture. Finally, if time permits, we shall present theoretical results regarding the identifiability and convergence rates of the latent
random clusters that arise in mixture based modeling.
Short Bio: Long Nguyen is associate professor in the Department of Statistics and, by courtesy, Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor. He received his PhD from the University of California, Berkeley in 2007 under the supervision of Michael I
Jordan and Martin Wainwright. Nguyen's interests include Bayesian nonparametric statistics, machine learning for distributed and complex systems, as well as applications in signal processing and environmental sciences. He is a recipient of the Leon O. Chua Award from UC Berkeley for his graduate work, the IEEE Signal
Processing Society's Young Author best paper award, the CAREER award from the NSF's Division of Mathematical Sciences, and a couple of best paper awards from the International Conference on Machine Learning (ICML) in 2004 and 2014.
*Join us for light refreshments and meet our guest from 3:45 to 4:00 in the lobby of Duncan Hall. The colloquium begins at 4:00 and ends at 5:00.
Monday, October 3, 2016
4:00 PM to 5:00 PM
Duncan Hall, RM1070