Prediction-Powered Adaptive Shrinkage Estimation
This work addresses the need for efficient statistical estimation in modern applications with numerous parallel questions, representing an incremental improvement by integrating existing frameworks.
The paper tackled the problem of estimating multiple means in large-scale statistical applications by combining Prediction-Powered Inference with empirical Bayes shrinkage, resulting in a method that adapts to ML prediction reliability and outperforms baselines in experiments.
Prediction-Powered Inference (PPI) is a powerful framework for enhancing statistical estimates by combining limited gold-standard data with machine learning (ML) predictions. While prior work has demonstrated PPI's benefits for individual statistical problems, modern applications require answering numerous parallel statistical questions. We introduce Prediction-Powered Adaptive Shrinkage (PAS), a method that bridges PPI with empirical Bayes shrinkage to improve the estimation of multiple means. PAS debiases noisy ML predictions within each task and then borrows strength across tasks by using those same predictions as a reference point for shrinkage. The amount of shrinkage is determined by minimizing an unbiased estimate of risk, and we prove that this tuning strategy is asymptotically optimal. Experiments on both synthetic and real-world datasets show that PAS adapts to the reliability of the ML predictions and outperforms traditional and modern baselines in large-scale applications.