  Self-training based semi-supervised learning algorithms have enabled the
learning of highly accurate deep neural networks, using only a fraction of
labeled data. However, the majority of work on self-training has focused on the
objective of improving accuracy, whereas practical machine learning systems can
have complex goals (e.g. maximizing the minimum of recall across classes, etc.)
that are non-decomposable in nature. In this work, we introduce the
Cost-Sensitive Self-Training (CSST) framework which generalizes the
self-training-based methods for optimizing non-decomposable metrics. We prove
that our framework can better optimize the desired non-decomposable metric
utilizing unlabeled data, under similar data distribution assumptions made for
the analysis of self-training. Using the proposed CSST framework, we obtain
practical self-training methods (for both vision and NLP tasks) for optimizing
different non-decomposable metrics using deep neural networks. Our results
demonstrate that CSST achieves an improvement over the state-of-the-art in
majority of the cases across datasets and objectives.
