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With the ``discovery'' of copulas by machine learning researchers, several works have emerged that focus on the high-dimensional scenario. This talk will provide a brief overview of these works and cover tree-averaged distributions (Kirshner), the nonparanormal (Liu, Lafferty and Wasserman), copula processes (Wilson and Ghahramani), kernel-based copula processes (Jaimungal and Ng), and copula networks (Elidan). Special emphasis will be given to the high level similarities and differences between these works.

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