Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revision
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| Format: | Preprint |
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2024
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| _version_ | 1866910471622230016 |
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| author | Dietrich, Stefan Rodemann, Julian Jansen, Christoph |
| author_facet | Dietrich, Stefan Rodemann, Julian Jansen, Christoph |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_15294 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revision Dietrich, Stefan Rodemann, Julian Jansen, Christoph Machine Learning Artificial Intelligence Statistics Theory Methodology 62C12 62C10 I.2.6; G.3 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. |
| title | Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revision |
| topic | Machine Learning Artificial Intelligence Statistics Theory Methodology 62C12 62C10 I.2.6; G.3 |
| url | https://arxiv.org/abs/2405.15294 |