  We present a new discriminative technique for the multiple-source adaptation,
MSA, problem. Unlike previous work, which relies on density estimation for each
source domain, our solution only requires conditional probabilities that can
easily be accurately estimated from unlabeled data from the source domains. We
give a detailed analysis of our new technique, including general guarantees
based on R\'enyi divergences, and learning bounds when conditional Maxent is
used for estimating conditional probabilities for a point to belong to a source
domain. We show that these guarantees compare favorably to those that can be
derived for the generative solution, using kernel density estimation. Our
experiments with real-world applications further demonstrate that our new
discriminative MSA algorithm outperforms the previous generative solution as
well as other domain adaptation baselines.
