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
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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.
The MLA-UCB algorithm uses ML-generated surrogate rewards from auxiliary data to provably lower cumulative regret in multi-armed bandits, achieving asymptotic optimality under joint Gaussian assumptions without requiring knowledge of the true-surrogate covariance.
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.
Active hypothesis testing framework uses auxiliary statistics for data-adaptive budget allocation to produce valid p-values or e-values with optimality under independence and admissibility under dependence.
Introduces D2S3 semiparametric framework that extends AIPW estimators to semi-supervised settings with MAR labeling, distribution shift, and decaying overlap, supplying corrected asymptotic rates instead of root-n convergence.
citing papers explorer
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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.
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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.
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Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards
The MLA-UCB algorithm uses ML-generated surrogate rewards from auxiliary data to provably lower cumulative regret in multi-armed bandits, achieving asymptotic optimality under joint Gaussian assumptions without requiring knowledge of the true-surrogate covariance.
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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.
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Active Hypothesis Testing under Computational Budgets with Applications to GWAS and LLM
Active hypothesis testing framework uses auxiliary statistics for data-adaptive budget allocation to produce valid p-values or e-values with optimality under independence and admissibility under dependence.
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Semiparametric semi-supervised learning for general targets under distribution shift and decaying overlap
Introduces D2S3 semiparametric framework that extends AIPW estimators to semi-supervised settings with MAR labeling, distribution shift, and decaying overlap, supplying corrected asymptotic rates instead of root-n convergence.