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Mondrian Processes

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We describe a novel stochastic process that can be used to construct a multidimensional generalization of Sethuraman’s stick-breaking construction. These Mondrian processes operate by starting with a multidimensional space, and recursively partitioning up the space using axis-aligned cuts. We describe how Mondrian processes can be applied to relational data modeling using the Aldous-Hoover representation for infinitely exchangeable arrays.

This talk is part of the Machine Learning @ CUED series.

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