A Note on the Prediction-Powered Bootstrap
Tijana Zrnic

TL;DR
PPBoot is a simple, versatile bootstrap-based method for prediction-powered inference that performs comparably or better than existing methods without needing asymptotic normality, broadening its applicability.
Contribution
Introduces PPBoot, a straightforward bootstrap method for prediction-powered inference applicable to diverse estimation problems without relying on asymptotic normality.
Findings
PPBoot performs nearly identically to PPI(++) in various examples.
PPBoot often outperforms PPI(++) when applicable.
PPBoot simplifies prediction-powered inference, especially where CLTs are difficult.
Abstract
We introduce PPBoot: a bootstrap-based method for prediction-powered inference. PPBoot is applicable to arbitrary estimation problems and is very simple to implement, essentially only requiring one application of the bootstrap. Through a series of examples, we demonstrate that PPBoot often performs nearly identically to (and sometimes better than) the earlier PPI(++) method based on asymptotic normalitywhen the latter is applicablewithout requiring any asymptotic characterizations. Given its versatility, PPBoot could simplify and expand the scope of application of prediction-powered inference to problems where central limit theorems are hard to prove.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Mining Algorithms and Applications · Data Management and Algorithms · Time Series Analysis and Forecasting
