PPI++: Efficient Prediction-Powered Inference

Autor:	Angelopoulos, Anastasios N., Duchi, John C., Zrnic, Tijana
Rok vydání:	2023
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	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. Comment: Code available at https://github.com/aangelopoulos/ppi_py
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.01453 Zobrazit plný text záznamu View this record from Arxiv