[Ml-stat-talks] Anima Anandkumar Thurs Feb 27, 4:30pm

David Blei blei at CS.Princeton.EDU
Tue Feb 25 09:21:51 EST 2014


dear ml-stat talks,

do not miss anima anandkumar explain how to fit latent variable models
with tensors, and the theoretical guarantees that go along with these
methods.  anima is a fantastic speaker.

best
dave



---------- Forwarded message ----------
From: Nicole E. Wagenblast <nwagenbl at cs.princeton.edu>
Date: Fri, Feb 21, 2014 at 6:00 AM
Subject: [talks] Colloquium Speaker Anima Anandkumar Thurs Feb 27, 4:30pm
To: "Talks (colloquium)" <talks at lists.cs.princeton.edu>


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

Anima Anandkumar, University of California Irvine
Thursday, February 27, 4:30pm
Computer Science 105

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.

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 Microsoft Faculty Fellowship, ARO Young Investigator
Award, NSF CAREER Award, IBM Fran Allen PhD fellowship, thesis award
from ACM SIGMETRICS society, paper awards from the ACM SIGMETRICS and
IEEE Signal Processing societies, and 2014 Sloan Fellowship.


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