# Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revision

arXiv: https://arxiv.org/abs/2405.15294 · HTML: https://mlsanity.com/paper/2405.15294/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 12 of 12)

- [In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning](https://mlsanity.com/paper/2101.06329/) (2021)
- [Priors in Bayesian Deep Learning: A Review](https://mlsanity.com/paper/2105.06868/) (2021)
- [Statistical Comparisons of Classifiers by Generalized Stochastic Dominance](https://mlsanity.com/paper/2209.01857/) (2022)
- [An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift](https://mlsanity.com/paper/2202.12123/) (2022)
- [Credal Self-Supervised Learning](https://mlsanity.com/paper/2106.11853/) (2021)
- [Accounting for Gaussian Process Imprecision in Bayesian Optimization](https://mlsanity.com/paper/2111.08299/) (2021)
- [Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement](https://mlsanity.com/paper/2306.12803/) (2023)
- [Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration](https://mlsanity.com/paper/2403.04629/) (2024)
- [Approximately Bayes-Optimal Pseudo Label Selection](https://mlsanity.com/paper/2302.08883/) (2023)
- [Bayesian data selection](https://mlsanity.com/paper/2109.02712/) (2021)
- [Pseudo Label Selection is a Decision Problem](https://mlsanity.com/paper/2309.13926/) (2023)
- [Improved Laplace Approximation for Marginal Likelihoods](https://mlsanity.com/paper/1502.06440/) (2015)

## Built on by (most cited first, 6 of 6)

- [Statistical Multicriteria Evaluation of LLM-Generated Text](https://mlsanity.com/paper/2506.18082/) (2025)
- [A Statistical Case Against Empirical Human-AI Alignment](https://mlsanity.com/paper/2502.14581/) (2025)
- [Generalization Bounds and Stopping Rules for Learning with Self-Selected Data](https://mlsanity.com/paper/2505.07367/) (2025)
- [Towards Bayesian Data Selection](https://mlsanity.com/paper/2406.12560/) (2024)
- [Reciprocal Learning](https://mlsanity.com/paper/2408.06257/) (2024)
- [Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification](https://mlsanity.com/paper/2605.12208/) (2026)

## Abstract (arXiv)

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.