  Classical Domain Adaptation methods acquire transferability by regularizing
the overall distributional discrepancies between features in the source domain
(labeled) and features in the target domain (unlabeled). They often do not
differentiate whether the domain differences come from the marginals or the
dependence structures. In many business and financial applications, the
labeling function usually has different sensitivities to the changes in the
marginals versus changes in the dependence structures. Measuring the overall
distributional differences will not be discriminative enough in acquiring
transferability. Without the needed structural resolution, the learned transfer
is less optimal. This paper proposes a new domain adaptation approach in which
one can measure the differences in the internal dependence structure separately
from those in the marginals. By optimizing the relative weights among them, the
new regularization strategy greatly relaxes the rigidness of the existing
approaches. It allows a learning machine to pay special attention to places
where the differences matter the most. Experiments on three real-world datasets
show that the improvements are quite notable and robust compared to various
benchmark domain adaptation models.
