Google Research

A Theory of Multiple-Source Adaptation with Limited Target Labeled Data

Abstract

We present a theoretical and algorithmic study of the multiple-source domain adaptation problem in the common scenario where the learner has access only to a limited amount of labeled target data, but where he has at his disposal a large amount of labeled data from multiple source domains. We show that a new family algorithms based on model selection ideas benefit from very favorable guarantees in this scenario and discuss some theoretical obstacles affecting some alternative techniques. We also report the results of several experiments with our algorithms that demonstrate their practical effectiveness in several tasks

Research Areas

Learn more about how we do research

We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work