Semi-Supervised Domain Adaptation with Non-Parametric Copulas
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 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.
Cite
Text
Lopez-paz et al. "Semi-Supervised Domain Adaptation with Non-Parametric Copulas." Neural Information Processing Systems, 2012.Markdown
[Lopez-paz et al. "Semi-Supervised Domain Adaptation with Non-Parametric Copulas." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/lopezpaz2012neurips-semisupervised/)BibTeX
@inproceedings{lopezpaz2012neurips-semisupervised,
title = {{Semi-Supervised Domain Adaptation with Non-Parametric Copulas}},
author = {Lopez-paz, David and Hernández-lobato, Jose M. and Schölkopf, Bernhard},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {665-673},
url = {https://mlanthology.org/neurips/2012/lopezpaz2012neurips-semisupervised/}
}