Prediction-Powered Adaptive Shrinkage Estimation

Sida Li, Nikolaos Ignatiadis
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:34836-34875, 2025.

Abstract

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 ($\texttt{PAS}$), a method that bridges PPI with empirical Bayes shrinkage to improve estimation of multiple means. $\texttt{PAS}$ debiases noisy ML predictions $\textit{within}$ each task and then borrows strength $\textit{across}$ 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 $\texttt{PAS}$ adapts to the reliability of the ML predictions and outperforms traditional and modern baselines in large-scale applications.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-li25ak, title = {Prediction-Powered Adaptive Shrinkage Estimation}, author = {Li, Sida and Ignatiadis, Nikolaos}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {34836--34875}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/li25ak/li25ak.pdf}, url = {https://proceedings.mlr.press/v267/li25ak.html}, abstract = {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 ($\texttt{PAS}$), a method that bridges PPI with empirical Bayes shrinkage to improve estimation of multiple means. $\texttt{PAS}$ debiases noisy ML predictions $\textit{within}$ each task and then borrows strength $\textit{across}$ 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 $\texttt{PAS}$ adapts to the reliability of the ML predictions and outperforms traditional and modern baselines in large-scale applications.} }
Endnote
%0 Conference Paper %T Prediction-Powered Adaptive Shrinkage Estimation %A Sida Li %A Nikolaos Ignatiadis %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-li25ak %I PMLR %P 34836--34875 %U https://proceedings.mlr.press/v267/li25ak.html %V 267 %X 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 ($\texttt{PAS}$), a method that bridges PPI with empirical Bayes shrinkage to improve estimation of multiple means. $\texttt{PAS}$ debiases noisy ML predictions $\textit{within}$ each task and then borrows strength $\textit{across}$ 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 $\texttt{PAS}$ adapts to the reliability of the ML predictions and outperforms traditional and modern baselines in large-scale applications.
APA
Li, S. & Ignatiadis, N.. (2025). Prediction-Powered Adaptive Shrinkage Estimation. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:34836-34875 Available from https://proceedings.mlr.press/v267/li25ak.html.

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