CUED Publications database

Semi-supervised domain adaptation with non-parametric copulas

Lopez-Paz, D and Hernández-Lobato, JM and Schölkopf, B (2012) Semi-supervised domain adaptation with non-parametric copulas. In: UNSPECIFIED pp. 665-673..

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Abstract

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 accross 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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:29
Last Modified: 03 Aug 2017 03:02
DOI: