Post-hoc calibration of miscalibrated black-box predictions on a labeled sample improves efficiency of prediction-powered inference for semisupervised mean estimation.
Predictions as surrogates: Revisiting surrogate outcomes in the age of ai.arXiv preprint arXiv:2501.09731
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
An adaptive budget allocation algorithm for LLM-augmented surveys learns question-level LLM reliability on the fly from human labels and reduces labeling waste from 10-12% to 2-6% compared to uniform allocation.
A framework models proxy-primary outcome discrepancies as random effects at the parameter level, estimated from aggregated historical observations to calibrate inferences under distribution shifts.
citing papers explorer
-
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.
-
Adaptive Budget Allocation in LLM-Augmented Surveys
An adaptive budget allocation algorithm for LLM-augmented surveys learns question-level LLM reliability on the fly from human labels and reduces labeling waste from 10-12% to 2-6% compared to uniform allocation.
-
Estimate Level Adjustment For Inference With Proxies Under Random Distribution Shifts
A framework models proxy-primary outcome discrepancies as random effects at the parameter level, estimated from aggregated historical observations to calibrate inferences under distribution shifts.