Active Learning by Discrepancy Minimization: A Comparison of Active Learning Methods Motivated by Generalization Bounds

In many settings in practice it is expensive to obtain labeled data while unlabeled data is abundant. This is problematic if one wants to train accurate (supervised) predictive models. The main idea behind active learning is that models can perform better with less labeled data, if the model may choose the data from which it learns. Active...