OPAL learns optimal smooth labeling policies from ML uncertainty scores to enable low-variance prediction-assisted inference with finite-sample coverage guarantees.
Double Robust Semi-Supervised Infer- ence for the Mean: Selection Bias under MAR Labeling with Decaying Overlap
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Optimized Labeling Resource Allocation for Prediction-Assisted Inference via OPAL
OPAL learns optimal smooth labeling policies from ML uncertainty scores to enable low-variance prediction-assisted inference with finite-sample coverage guarantees.