Correcting Sample Selection Bias by Unlabeled Data

We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias.Most algorithms for this setting try to first recover sampling distributions and then make appropriate corrections based on the distribution estimate.We present... View More