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Self-training is a simple yet effective method within semi-supervisedlearning. The idea is to iteratively enhance training data by addingpseudo-labeled data. Its generalization performance heavily depends on theselection of these pseudo-labeled data (PLS). In this paper, we aim atrendering PLS more robust towards the involved modeling assumptions. To thisend, we propose to select pseudo-labeled data that maximize a multi-objectiveutility function. The latter is constructed to account for different sources ofuncertainty, three of which we discuss in more detail: model selection,accumulation of errors and covariate shift. In the absence of second-orderinformation on such uncertainties, we furthermore consider the generic approachof the generalized Bayesian alpha-cut updating rule for credal sets. As apractical proof of concept, we spotlight the application of three of our robustextensions on simulated and real-world data. Results suggest that in particularrobustness w.r.t. model choice can lead to substantial accuracy gains.

In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised Learning