  We present a detailed theoretical analysis of the problem of multiple-source
adaptation in the general stochastic scenario, extending known results that
assume a single target labeling function. Our results cover a more realistic
scenario and show the existence of a single robust predictor accurate for
\emph{any} target mixture of the source distributions. Moreover, we present an
efficient and practical optimization solution to determine the robust predictor
in the important case of squared loss, by casting the problem as an instance of
DC-programming. We report the results of experiments with both an artificial
task and a sentiment analysis task. We find that our algorithm outperforms
competing approaches by producing a single robust model that performs well on
any target mixture distribution.
