  We present a novel instance-based approach to handle regression tasks in the
context of supervised domain adaptation under an assumption of covariate shift.
The approach developed in this paper is based on the assumption that the task
on the target domain can be efficiently learned by adequately reweighting the
source instances during training phase. We introduce a novel formulation of the
optimization objective for domain adaptation which relies on a discrepancy
distance characterizing the difference between domains according to a specific
task and a class of hypotheses. To solve this problem, we develop an
adversarial network algorithm which learns both the source weighting scheme and
the task in one feed-forward gradient descent. We provide numerical evidence of
the relevance of the method on public data sets for regression domain
adaptation through reproducible experiments.
