Unsupervised Domain Adaptation with Copula Models
This addresses domain adaptation for scenarios with no labeled target data, offering a novel approach to mitigate distribution discrepancies, though it appears incremental as it builds on existing adaptation methods.
The paper tackles unsupervised domain adaptation by using a copula-based regression framework to model conditional predictive densities and leverage Sklar's theorem for feature mapping, achieving more robust and accurate target label estimation on benchmark datasets compared to recent methods.
We study the task of unsupervised domain adaptation, where no labeled data from the target domain is provided during training time. To deal with the potential discrepancy between the source and target distributions, both in features and labels, we exploit a copula-based regression framework. The benefits of this approach are two-fold: (a) it allows us to model a broader range of conditional predictive densities beyond the common exponential family, (b) we show how to leverage Sklar's theorem, the essence of the copula formulation relating the joint density to the copula dependency functions, to find effective feature mappings that mitigate the domain mismatch. By transforming the data to a copula domain, we show on a number of benchmark datasets (including human emotion estimation), and using different regression models for prediction, that we can achieve a more robust and accurate estimation of target labels, compared to recently proposed feature transformation (adaptation) methods.