Boosting with Multiple Sources
Datasets and Benchmarks Track
Authors: Corinna Cortes, Mehryar Mohri, Dmitry Storcheus, Ananda Theertha Suresh

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.