Semi-Supervised Domain Adaptation with Non-Parametric Copulas

author: David Lopez-Paz, Max Planck Institute for Intelligent Systems, Max Planck Institute
published: Jan. 14, 2013,   recorded: December 2012,   views: 261
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Description

A new framework based on the theory of copulas is proposed to address semi-supervised domain adaptation problems. The presented method factorizes any multivariate density into a product of marginal distributions and bivariate copula functions. Therefore, changes in each of these factors can be detected and corrected to adapt a density model across different learning domains. Importantly, we introduce a novel vine copula model, which allows for this factorization in a non-parametric manner. Experimental results on regression problems with real-world data illustrate the efficacy of the proposed approach when compared to state-of-the-art techniques.

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Download slides icon Download slides: machine_lopez_paz_domain_adaptation_01.pdf (363.6 KB)

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