Work

PPI++: Efficient prediction-powered inference

Year: 2026

Type: article

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.

Source: The Annals of Applied Statistics

DOI: 10.1214/26-aoas2215

Authors: Anastasios N. Angelopoulos, John C. Duchi, Tijana Zrnic

Institutions: University of California, Berkeley, Stanford University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: Zayed University, Office of Naval Research

Awards: G4720003262, G8876996369