Summary of Semi-supervised Learning Guided by the Generalized Bayes Rule Under Soft Revision, By Stefan Dietrich et al.
Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revision
by Stefan Dietrich, Julian Rodemann, Christoph Jansen
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The researchers investigate the Gamma-Maximin method with soft revision as a robust criterion for pseudo-label selection in semi-supervised learning. They use credal sets of priors to represent epistemic modeling uncertainty and update them using the Gamma-Maximin method with soft revision. The authors formalize the task of finding optimal pseudo-labeled data as an optimization problem, which they implement for logistic models. Results show that the method achieves promising results, especially when the proportion of labeled data is low. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at a new way to choose fake labels in semi-supervised learning called Gamma-Maximin with soft revision. It’s trying to figure out how to make this work better by using special math tools called credal sets. The authors turn it into an optimization problem and test it on some models. They find that it does well, especially when there aren’t many real labels. |
Keywords
» Artificial intelligence » Optimization » Semi supervised
