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The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities

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STSW01 - Theoretical and algorithmic underpinnings of Big Data

Non-parametric multivariate density estimation faces strong statistical and computational bottlenecks, and the more practical approaches impose near-parametric assumptions on the form of the density functions. In this paper, we leverage recent developments in parametric graphical models to propose a class of non-parametric multivariate densities which have very attractive computational and statistical properties. Our approach relies on the simple function space assumption that the conditional distribution of each variable conditioned on the other variables has a non-parametric exponential family form. Joint work with Mladen Kolar, Arun Sai Suggala.

This talk is part of the Isaac Newton Institute Seminar Series series.

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