Summary of Cross-validation Conformal Risk Control, by Kfir M. Cohen et al.
Cross-Validation Conformal Risk Control
by Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone, Shlomo Shamai
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty Summary: This paper proposes a novel conformal risk control (CRC) method called cross-validation CRC (CV-CRC), which extends the jackknife-minmax from conformal prediction (CP) to CRC. Unlike traditional CRC, which requires splitting available data into training and validation sets, CV-CRC uses cross-validation to ensure calibration guarantees for a broader range of risk functions. Theoretical analysis shows that CV-CRC provides average risk guarantees for the set predictor, while numerical experiments demonstrate its ability to reduce average set size when data are limited. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper introduces a new way to make sure predictions are reliable and accurate. It’s called conformal risk control (CRC). Right now, CRC needs a lot of data to work well. But this new method, cross-validation CRC (CV-CRC), uses the available data more efficiently. It makes sure that the predictions are good and reduces the amount of data needed. This is important because sometimes we don’t have enough data. |
