Semi-supervised empirical risk minimization: Using unlabeled data to improve prediction

@article{Yuval2022SemisupervisedER,
title={Semi-supervised empirical risk minimization: Using unlabeled data to improve prediction},
author={Oren Yuval and Saharon Rosset},
journal={Electronic Journal of Statistics},
year={2022}
}
• Published 1 September 2020
• Computer Science
• Electronic Journal of Statistics
We present a general methodology for using unlabeled data to design semi supervised learning (SSL) variants of the Empirical Risk Minimization (ERM) learning process. Focusing on generalized linear regression, we analyze of the effectiveness of our SSL approach in improving prediction performance. The key ideas are carefully considering the null model as a competitor, and utilizing the unlabeled data to determine signal-noise combinations where SSL outperforms both supervised learning and the…

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