Guaranteed Learning of Latent Variable Models: Overlapping Community Models and Overcomplete Representations

Speaker: Anima Anandkumar

Date: Monday, February 24, 2014

Time: 4:00 PM to 5:00 PM Note: all times are in the Eastern Time Zone

Refreshments: 3:45 PM

Public: Yes

Location: 32-G449

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Host: Devavrat Shah

Contact: Francis Doughty, 3-4602, francisd@csail.mit.edu

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Reminder Subject: TALK: Guaranteed Learning of Latent Variable Models: Overlapping Community Models and Overcomplete Representations

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 membership 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
orders 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. Identifiability and efficient
learning are established under some natural conditions such as
incoherent dictionaries or persistent topics.

Bio: 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
at MIT between 2009-2010. She is the recipient of the 2014 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.

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Created by Francis Doughty at Monday, February 03, 2014 at 4:48 PM.