Robust Semi-supervised Learning via $f$-Divergence and $\alpha$-R\'enyi Divergence
<|reference_start|>Robust Semi-supervised Learning via $f$-Divergence and $\alpha$-R\'enyi Divergence: This paper investigates a range of empirical risk functions and regularization methods suitable for self-training methods in semi-supervised learning. These approaches draw inspiration from various divergence measures, such as $f$-divergences and $\alpha$-R\'enyi divergences. Inspired by the theoretical foundations rooted in divergences, i.e., $f$-divergences and $\alpha$-R\'enyi divergence, we also provide valuable insights to enhance the understanding of our empirical risk functions and regularization techniques. In the pseudo-labeling and entropy minimization techniques as self-training methods for effective semi-supervised learning, the self-training process has some inherent mismatch between the true label and pseudo-label (noisy pseudo-labels) and some of our empirical risk functions are robust, concerning noisy pseudo-labels. Under some conditions, our empirical risk functions demonstrate better performance when compared to traditional self-training methods.<|reference_end|>
@article{aminian2024robust,
    title={Robust Semi-supervised Learning via $f$-Divergence and $\alpha$-R\'enyi
  Divergence},
    author={Gholamali Aminian, Amirhossien Bagheri, Mahyar JafariNodeh, Radmehr
  Karimian, Mohammad-Hossein Yassaee},
    journal={arXiv preprint arXiv:2405.00454},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.00454},
    primaryClass={cs.LG cs.IT math.IT stat.ML}
}