PPI++: Efficient Prediction-Powered Inference
Machine Learning
2024-03-27 v2 Machine Learning
Methodology
Abstract
We present PPI++: a computationally lightweight methodology for estimation and inference based on a small labeled dataset and a typically much larger dataset of machine-learning predictions. The methods automatically adapt to the quality of available predictions, yielding easy-to-compute confidence sets -- for parameters of any dimensionality -- that always improve on classical intervals using only the labeled data. PPI++ builds on prediction-powered inference (PPI), which targets the same problem setting, improving its computational and statistical efficiency. Real and synthetic experiments demonstrate the benefits of the proposed adaptations.
Cite
@article{arxiv.2311.01453,
title = {PPI++: Efficient Prediction-Powered Inference},
author = {Anastasios N. Angelopoulos and John C. Duchi and Tijana Zrnic},
journal= {arXiv preprint arXiv:2311.01453},
year = {2024}
}
Comments
Code available at https://github.com/aangelopoulos/ppi_py