AI RESEARCH
Calibeating Prediction-Powered Inference
arXiv CS.LG
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ArXi:2604.21260v1 Announce Type: cross 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, 1994], which protects against prediction-model misspecification but can be inefficient when the prediction score is poorly aligned with the outcome scale. We