Summary of Training-conditional Coverage Bounds Under Covariate Shift, by Mehrdad Pournaderi and Yu Xiang
Training-Conditional Coverage Bounds under Covariate Shift
by Mehrdad Pournaderi, Yu Xiang
First submitted to arxiv on: 26 May 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 A machine learning paper explores the concept of training-conditional coverage guarantees in conformal prediction under covariate shift. The authors study the concentration of error distributions below a nominal level, conditional on the training data, for various conformal prediction methods. They develop a weighted version of the Dvoretzky-Kiefer-Wolfowitz inequality to analyze the training-conditional coverage properties of these methods. The results show that the split conformal method is almost assumption-free, while the full conformal and jackknife+ methods rely on strong assumptions about the uniform stability of the training algorithm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Conformal prediction is a way for machines to make predictions while explaining their uncertainty. In this paper, researchers study how well different conformal prediction methods work when there’s a change in what kind of data they’re looking at. They develop new tools to understand these methods and find out which ones are reliable without making big assumptions. |
Keywords
» Artificial intelligence » Machine learning
