Semi-Supervised Domain Adaptation with Non-Parametric Copulas
This addresses domain adaptation problems for machine learning applications, but appears incremental as it builds on existing copula theory with a novel non-parametric extension.
The paper tackled semi-supervised domain adaptation by proposing a framework based on copulas to factorize multivariate densities and detect changes across domains, achieving efficacy compared to state-of-the-art techniques in regression on real-world data.
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 cop- ula functions. Therefore, changes in each of these factors can be detected and corrected to adapt a density model accross different learning domains. Impor- tantly, 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.