Incorporating latent or hidden variables is a crucial aspect of statistical modeling. I will present a statistical and a computational framework for guaranteed learning of a wide range of latent variable models. I will focus on two instances, viz., community detection and overcomplete representations.
The goal of community detection is to discover hidden communities from graph data. I will present a tensor decomposition approach for learning probabilistic mixed mem-bership models. The tensor approach is guaranteed to correctly recover the mixed membership communities with tight guarantees. We have deployed it on many real-world networks, e.g. Facebook, Yelp and DBLP. It is easily parallelizable, and is or-ders of magnitude faster than the state-of-art stochastic variational approach.
I will then discuss recent results on learning overcomplete latent representations, where the latent dimensionality can far exceed the observed dimensionality. I will present two frameworks, viz., sparse coding and sparse topic modeling. Identifi-ability and efficient learning are established under some natural conditions such as incoherent dictionaries or persistent topics.
Anima Anandkumar is a faculty at 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
sharonw [atsymbol] cs.cmu.edu