Summary of Semi-supervised Risk Control Via Prediction-powered Inference, by Bat-sheva Einbinder et al.
Semi-Supervised Risk Control via Prediction-Powered Inference
by Bat-Sheva Einbinder, Liran Ringel, Yaniv Romano
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 RCPS framework is a machine learning tool that transforms model outputs into predictive rules with controlled error rates. The approach uses labeled hold-out data to tune hyper-parameters, but limited data can lead to noisy results and overly conservative predictions. To address this issue, the authors introduce a semi-supervised calibration procedure that leverages unlabeled data without compromising statistical validity. This technique builds upon the prediction-powered inference framework, tailored for risk-controlling tasks. The authors demonstrate the effectiveness of their proposal through experiments in few-shot image classification and early time series classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The RCPS framework helps machine learning models make better predictions by controlling errors. It uses some extra data to fine-tune its settings. This is helpful when we have limited data, which can lead to mistakes. The authors also showed that their new approach works well in practice with real-world images and time series data. |
Keywords
* Artificial intelligence * Classification * Few shot * Image classification * Inference * Machine learning * Semi supervised * Time series
