We present a new algorithm for domain adaptation improving upon a discrepancy mini-
mization algorithm, (DM), previously shown to outperform a number of algorithms for
this problem. Unlike many previous proposed solutions for domain adaptation, our
algorithm does not consist of a fixed reweighting of the losses over the training
sample. Instead, the reweighting depends on the hypothesis sought. The algorithm is
derived from a less con- servative notion of discrepancy than the DM algorithm. We
call this quantity generalized discrepancy. We present a detailed description of
our algorithm and show that it can be formulated as a convex optimization problem.
We also give a detailed theoretical analysis of its learning guarantees which helps
us select its parameters. Finally, we report the results of experiments
demonstrating that it improves upon discrepancy minimization in several tasks.