We present a new algorithm for domain adaptation improving upon a discrepancy minimization algorithm previously shown to outperform a number of algorithms for this task. Unlike many previous algorithms for domain adaptation, our algorithm does not consist of a fixed reweighting of the losses over the training sample. We show that our algorithm benefits from a solid theoretical foundation and more favorable learning bounds than discrepancy minimization. We present a detailed description of our algorithm and give several efficient solutions for solving its optimization problem. We also report the results of several experiments showing that it outperforms discrepancy minimization.
@article{arxiv.1405.1503,
title = {Adaptation Algorithm and Theory Based on Generalized Discrepancy},
author = {Corinna Cortes and Mehryar Mohri and Andres Muñoz Medina},
journal= {arXiv preprint arXiv:1405.1503},
year = {2015}
}