Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revision
<|reference_start|>Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revision: We provide a theoretical and computational investigation of the Gamma-Maximin method with soft revision, which was recently proposed as a robust criterion for pseudo-label selection (PLS) in semi-supervised learning. Opposed to traditional methods for PLS we use credal sets of priors ("generalized Bayes") to represent the epistemic modeling uncertainty. These latter are then updated by the Gamma-Maximin method with soft revision. We eventually select pseudo-labeled data that are most likely in light of the least favorable distribution from the so updated credal set. We formalize the task of finding optimal pseudo-labeled data w.r.t. the Gamma-Maximin method with soft revision as an optimization problem. A concrete implementation for the class of logistic models then allows us to compare the predictive power of the method with competing approaches. It is observed that the Gamma-Maximin method with soft revision can achieve very promising results, especially when the proportion of labeled data is low.<|reference_end|>
@article{dietrich2024semi-supervised,
    title={Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft
  Revision},
    author={Stefan Dietrich, Julian Rodemann and Christoph Jansen},
    journal={arXiv preprint arXiv:2405.15294},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15294},
    primaryClass={stat.ML cs.AI cs.LG math.ST stat.ME stat.TH}
}