Summary of Another Look at Inference After Prediction, by Jessica Gronsbell et al.
Another look at inference after prediction
by Jessica Gronsbell, Jianhui Gao, Yaqi Shi, Zachary R. McCaw, David Cheng
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 The paper revisits two key approaches to prediction-based inference (PB) in machine learning, which aims to mitigate bias from errors in predictions and improve efficiency. Specifically, the authors investigate Prediction-powered Inference (PPI), a method proposed by Angelopoulos et al. in 2023, and find that it does not achieve the goal of improving efficiency. The authors then propose a simple modification to PPI that provides theoretically justified improvements in efficiency. This is achieved through an analysis of UK Biobank data and a review of classical methods from economics and statistics literature dating back to the 1960s. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machine learning models can help scientists make predictions faster and more accurately. Right now, scientists are using these models to make predictions in areas like biology and epidemiology. The problem is that these models can be biased because they’re not perfect. To fix this, scientists are using something called prediction-based inference (PB). This method tries to use a lot of predictions together with a little bit of actual data to get better results. In this paper, the authors look at one way to do PB and find out that it’s not as good as they thought. They then propose a new way to do PB that is better. The authors also talk about how some old ideas from economics and statistics can be used to make PB work even better. |
Keywords
» Artificial intelligence » Inference » Machine learning
