Unsupervised domain adaptation with copula models

@article{Tran2017UnsupervisedDA,
  title={Unsupervised domain adaptation with copula models},
  author={Cuong D. Tran and Ognjen Rudovic and Vladimir Pavlovic},
  journal={2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)},
  year={2017},
  pages={1-6}
}
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… 
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