Semi-Supervised Domain Adaptation with Non-Parametric Copulas

TL;DR

This paper introduces a copula-based framework for semi-supervised domain adaptation, enabling flexible density modeling and correction across domains, demonstrated with real-world regression data.

Contribution

It proposes a novel vine copula model for non-parametric factorization of multivariate densities in domain adaptation.

Findings

Effective in real-world regression tasks

Outperforms state-of-the-art techniques

Flexible non-parametric density modeling

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 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.