Calibeating Prediction-Powered Inference
Learn to improve semisupervised mean estimation using Calibeating Prediction-Powered Inference, which calibrates black-box prediction models for more accurate results
- Apply Calibeating Prediction-Powered Inference to semisupervised mean estimation tasks
- Use augmented inverse-probability weighting (AIPW) as a baseline for comparison
- Evaluate the performance of Calibeating against AIPW in terms of efficiency and accuracy
- Calibrate the output of a black-box prediction model using Calibeating
- Test the calibrated model on a holdout set to estimate its performance
Data scientists and machine learning engineers working on semisupervised learning tasks can benefit from this technique to improve the accuracy of their models, especially when dealing with small labeled samples and large unlabeled samples
💡 Calibeating can calibrate black-box prediction models to improve the accuracy of semisupervised mean estimation, especially when the prediction score is poorly aligned with the outcome scale
Improve semisupervised mean estimation with Calibeating Prediction-Powered Inference! #machinelearning #semisupervisedlearning
Key Takeaways
Learn to improve semisupervised mean estimation using Calibeating Prediction-Powered Inference, which calibrates black-box prediction models for more accurate results
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Abstract:
arXiv:2604.21260v1 Announce Type: cross Abstract: We study semisupervised mean estimation with a small labeled sample, a large unlabeled sample, and a black-box prediction model whose output may be miscalibrated. A standard approach in this setting is augmented inverse-probability weighting (AIPW) [Robins et al., 1994], which protects against prediction-model misspecification but can be inefficient when the prediction score is poorly aligned with the outcome scale. We introduce Calibrated Predic
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