A. Ismail

Self-Improvement as Coherence Optimization: A Theoretical Account

It is proved that coherence optimization is equivalent to description-length regularization, and that among all such regularization schemes, coherence regularization with a prior derived from a pretrained model optimizes a lower bound of worst-case accuracy for semi-supervised learning.