[Ml-stat-talks] Fwd: [talks] P Gopalan preFPO

David Blei blei at CS.Princeton.EDU
Fri Apr 11 16:16:28 EDT 2014


all,

the CS department "pre-FPO" is open to the public.  next week is my
student, prem gopalan, who will be discussing large scale variational
inference for discrete data.  see below.  this will be particularly
interesting to those of you working on large scale applications of
probabilistic models for science and technology.

best
dave


---------- Forwarded message ----------
From: Melissa M. Lawson <mml at cs.princeton.edu>
Date: Fri, Apr 11, 2014 at 3:30 PM
Subject: [talks] P Gopalan preFPO
To: talks <talks at lists.cs.princeton.edu>



Prem Gopalan will present his preFPO on Friday April 18 at 3PM
in Room 402.  The members of his committee are:  David Blei,
advisor; Rob Schapire and Jake Hofman (MSR), readers; Michael
Freedman and John Storey (MOL), nonreaders.  Everyone is invited
to attend his talk.  His abstract follows below.
----------------

Title: Scalable inference of discrete outcomes: networks, genotype and
user consumption

Latent variable models are probabilistic models that can be used to
extract hidden structure in real data. They are important in many
fields such as genetics, social network analysis and collaborative
filtering. Data analysis using these models is useful in making
predictions, exploring the data and in making better models. Will
inference algorithms be able to cope with the scale of modern data
sets? If yes, what properties of the model, data and the algorithms
help in achieving scalability?

In this talk, I will present advances in statistical models and
scalable inference algorithms for identifying overlapping communities
in networks, ancestral populations in human genetic variations, and
latent structure in user consumption data. These algorithms lie in the
framework of variational inference, an approach to approximate
posterior inference that has been adapted to a variety of
probabilistic models. As a detailed example, I will present
hierarchical Poisson matrix factorization models for recommendation,
and a corresponding variational inference algorithm. The algorithm
scales to more than 100 million observations on a single CPU and
predicts better than prior methods. A simple extension to this model
allows for cold-start recommendations. I will end with a novel
Bayesian nonparametric variant of Poisson matrix factorization that
eases the burden of searching for the best number of latent
components. This talk includes ongoing work.
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