title: Boosting with Multiple Sources
abstract:  We study the problem of learning accurate ensemble predictors, in
    particular boosting, in the presence of multiple source domains. We
    show that the standard convex combination ensembles in general
    cannot succeed in this scenario and adopt instead a domain-weighted
    combination. We introduce and analyze a new boosting algorithm,
    MULTIBOOST, for this scenario and show that it benefits from
    favorable theoretical guarantees. We also report the results of
    several experiments with our algorithm demonstrating that it
    outperforms natural baselines on multi-source text-based,
    image-based and tabular data. We further present an extension of our
    algorithm to the federated learning scenario and report favorable
    experimental results for that setting as well. Additionally, we
    describe in detail an extension of our algorithm to the multi-class
    setting, MCMULTIBOOST, for which we also report
    experimental results.
authors: Corinna Cortes Mehryar Mohri Dmitry Storcheus Ananda Theertha Suresh
hIndex: Corinna Cortes 44