Summary of A Note on the Prediction-powered Bootstrap, by Tijana Zrnic
A Note on the Prediction-Powered Bootstrap
by Tijana Zrnic
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper introduces a new method called PPBoot for prediction-powered inference. It is a simple and versatile approach that can be applied to various estimation problems, requiring only one bootstrap iteration. The authors demonstrate through examples that PPBoot often performs similarly or better than the existing PPI(++) method based on asymptotic normality, without relying on asymptotic characterizations. Due to its flexibility, PPBoot has the potential to expand the scope of application in cases where central limit theorems are challenging to prove. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PPBoot is a new way to make predictions and draw conclusions from data. It’s easy to use and can be applied to many different problems. The researchers compared PPBoot to another method called PPI(++) and found that it often works just as well or even better, without needing special assumptions about the data. This could help solve problems where traditional methods don’t work. |
Keywords
» Artificial intelligence » Inference
