  When learning a mapping from an input space to an output space, the
assumption that the sample distribution of the training data is the same as
that of the test data is often violated. Unsupervised domain shift methods
adapt the learned function in order to correct for this shift. Previous work
has focused on utilizing unlabeled samples from the target distribution. We
consider the complementary problem in which the unlabeled samples are given
post mapping, i.e., we are given the outputs of the mapping of unknown samples
from the shifted domain. Two other variants are also studied: the two sided
version, in which unlabeled samples are give from both the input and the output
spaces, and the Domain Transfer problem, which was recently formalized. In all
cases, we derive generalization bounds that employ discrepancy terms.
