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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.

Keywords

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