  In the problem of domain adaptation for binary classification, the learner is
presented with labeled examples from a source domain, and must correctly
classify unlabeled examples from a target domain, which may differ from the
source. Previous work on this problem has assumed that the performance measure
of interest is the expected value of some loss function. We introduce a new
Neyman-Pearson-like criterion and argue that, for this optimality criterion,
stronger domain adaptation results are possible than what has previously been
established. In particular, we study a class of domain adaptation problems that
generalizes both the covariate shift assumption and a model for
feature-dependent label noise, and establish optimal classification on the
target domain despite not having access to labelled data from this domain.
