Summary of Adjusting Regression Models For Conditional Uncertainty Calibration, by Ruijiang Gao et al.
Adjusting Regression Models for Conditional Uncertainty Calibration
by Ruijiang Gao, Mingzhang Yin, James McInerney, Nathan Kallus
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes a novel algorithm to improve the conditional coverage of conformal prediction methods, which typically offer finite-sample distribution-free marginal coverage guarantees but lack conditional coverage guarantees important for high-stakes decisions. The proposed algorithm trains a regression function to control the miscoverage gap between the conditional and nominal coverage rates after applying the split conformal prediction procedure. Empirical demonstrations on synthetic and real-world datasets show the efficacy of this method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making predictions more reliable by improving how we account for uncertainty. Right now, some methods can predict the average value of something with a certain level of accuracy, but they don’t do as well when it comes to predicting individual values or specific outcomes. The authors come up with a new way to combine different prediction models to get better results. They show that this approach works well on both fake and real datasets. |
Keywords
» Artificial intelligence » Regression
