PUMA uses model averaging to jointly handle uncertainties from model misspecification, tuning, and ML choice, delivering asymptotic in-sample and out-of-sample prediction optimality plus estimation consistency.
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Post-hoc calibration of miscalibrated black-box predictions on a labeled sample improves efficiency of prediction-powered inference for semisupervised mean estimation.
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Calibeating Prediction-Powered Inference
Post-hoc calibration of miscalibrated black-box predictions on a labeled sample improves efficiency of prediction-powered inference for semisupervised mean estimation.