  In many real-world applications, we want to exploit multiple source datasets
of similar tasks to learn a model for a different but related target dataset --
e.g., recognizing characters of a new font using a set of different fonts.
While most recent research has considered ad-hoc combination rules to address
this problem, we extend previous work on domain discrepancy minimization to
develop a finite-sample generalization bound, and accordingly propose a
theoretically justified optimization procedure. The algorithm we develop,
Domain AggRegation Network (DARN), is able to effectively adjust the weight of
each source domain during training to ensure relevant domains are given more
importance for adaptation. We evaluate the proposed method on real-world
sentiment analysis and digit recognition datasets and show that DARN can
significantly outperform the state-of-the-art alternatives.
