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.

我们提供了对带软修订的 Gamma-Maximin 方法进行理论和计算研究的结果，该方法最近被提出作为半监督学习中伪标签选择（PLS）的稳健准则。与传统的 PLS 方法相反，我们使用先验的可信集（“广义贝叶斯”）来表示认知建模的不确定性，然后通过带软修订的 Gamma-Maximin 方法来更新这些先验集合。最终，我们从更新后的可信集中选择最有可能的伪标记数据，以反映最不利分布的光线。我们将根据 Gamma-Maximin 方法与其他方法进行比较构建了一个关于 logistic 模型类的具体实现，以比较方法的预测能力。观察结果表明，特别是当标记数据的比例较低时，带软修订的 Gamma-Maximin 方法可以取得非常有希望的结果。