Principled Approaches for Learning Latent Variable Models

In any learning task, it is natural to incorporate latent or hidden variables which are not directly observed. For instance, in a social network, we can observe interactions among the actors, but not their hidden interests/intents, in gene networks, we can measure gene expression levels but not the detailed regulatory mechanisms, and so on. I will present a broad framework for unsupervised learning of latent variable models, addressing both statistical and computational concerns. We show that higher order relationships among observed variables have a low rank representation under natural statistical constraints such as conditional-independence relationships. We also present efficient computational methods for finding these low rank representations. These findings have implications in a number of settings such as finding hidden communities in networks, discovering topics in text documents and learning about gene regulation in computational biology. I will also present principled approaches for learning overcomplete models, where the latent dimensionality can be much larger than the observed dimensionality, under natural sparsity constraints. This has implications in a number of applications such as sparse coding and feature learning.

Speaker Details

Anima Anandkumar is on the faculty of the EECS Dept. at U.C.Irvine since August 2010. Her research interests are in the area of large-scale machine learning and high-dimensional statistics. She received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She has been a visiting faculty at Microsoft Research New England in 2012 and a postdoctoral researcher at the Stochastic Systems Group at MIT between 2009-2010. She is the recipient of the Alfred.P. Sloan Fellowship Microsoft Faculty Fellowship, ARO Young Investigator Award, NSF CAREER Award, IBM Fran Allen PhD fellowship, thesis award from ACM SIGMETRICS society, and paper awards from the ACM SIGMETRICS and IEEE Signal Processing societies.

Date:
Speakers:
Anima Anandkumar
Affiliation:
UC Irvine