NIPS-2011 Workshop, Granada, Spain,
December 16, 2011


From high-throughput biology and astronomy to voice analysis and medical diagnosis,
a wide variety of complex domains are inherently continuous and high dimensional.
The statistical framework of copulas offers a flexible tool for modeling highly non-linear
multivariate distributions for continuous data. Copulas are a theoretically and
practically important tool from statistics that explicitly allow one to separate the
dependency structure between random variables from their marginal distributions.
Although bivariate copulas are a widely used tool in finance, and have even been famously
accused of "bringing the world financial system to its knees" (Wired Magazine, 2009),
the use of copulas for high dimensional data is in its infancy.

While studied in statistics for many years, copulas have only recently been noticed by a
number of machine learning researchers, with this "new" tool appearing in the recent
leading machine learning conferences (ICML, UAI and NIPS). The goal of this workshop is
to promote the further understanding and development of copulas for the kinds of complex
modeling tasks that are the focus of machine learning. Specifically, the goals of the workshop are to:

The target audience includes leading researchers from academia and
industry, with the aim of facilitating cross fertilization between
different perspectives.


We invite submission of abstracts to the workshop.
Abstracts will be selected for a short oral or poster presentation.


Submission deadline: Friday, October 21st, 2011.
Notification of acceptance: Friday, November 4th, 2011.


Gal Elidan, The Hebrew University of Jeruslaem
Zoubin Ghahramani Cambridge University and Carnegie Mellon University
John Lafferty, University of Chicago and Carnegie Mellon University
Last modified: Wed Nov 16 16:10:59 IST 2011