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Copulas in Machine Learning
Gal Elidan · Zoubin Ghahramani · John Lafferty

Thu Dec 15 10:30 PM -- 11:00 AM (PST) @ Melia Sierra Nevada: Genil
Event URL: http://pluto.huji.ac.il/~galelidan/CopulaWorkshop/index.html »

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, Feb. 23, 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 machine 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.

Author Information

Gal Elidan (Hebrew University)
Zoubin Ghahramani (Uber and University of Cambridge)

Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group. He studied computer science and cognitive science at the University of Pennsylvania, obtained his PhD from MIT in 1995, and was a postdoctoral fellow at the University of Toronto. His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience Unit in London, and as a faculty member of CMU's Machine Learning Department for over 10 years. His current research interests include statistical machine learning, Bayesian nonparametrics, scalable inference, probabilistic programming, and building an automatic statistician. He has held a number of leadership roles as programme and general chair of the leading international conferences in machine learning including: AISTATS (2005), ICML (2007, 2011), and NIPS (2013, 2014). In 2015 he was elected a Fellow of the Royal Society.

John Lafferty (Yale University)

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