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:

  • draw the attention of machine learning researchers to the important framework of copulas
  • provide a theoretical and practical introduction to copulas
  • identify promising research problems in machine learning that could exploit copulas
  • bring together researchers from the statistics and learning communities working in this area.

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


  • Gal Elidan, The Hebrew University of Jerusalem
  • Zoubin Ghahramani Cambridge University and Carnegie Mellon University
  • John Lafferty, University of Chicago and Carnegie Mellon University