Summary of Cap: a General Algorithm For Online Selective Conformal Prediction with Fcr Control, by Yajie Bao et al.
CAP: A General Algorithm for Online Selective Conformal Prediction with FCR Control
by Yajie Bao, Yuyang Huo, Haojie Ren, Changliang Zou
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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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 proposed paper, CAP (Calibration after Adaptive Pick), addresses the problem of post-selection predictive inference in online tasks. The authors develop a framework that adapts to the selection process by constructing a calibration set for each selected individual and then outputs conformal prediction intervals. The approach achieves an exact selection-conditional coverage guarantee in finite-sample and distribution-free regimes. To account for distribution shifts in online data, CAP is integrated with dynamic conformal prediction algorithms, ensuring long-run control of false coverage-statement rates (FCR). Numerical results on synthetic and real datasets demonstrate that CAP effectively controls FCR and yields narrower prediction intervals compared to existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CAP is a new method for making predictions online. Imagine you’re trying to predict what someone will do next, but you only get to see their actions after they’ve already made the decision. To make accurate predictions, you need to adjust your approach based on past successes and failures. The CAP method does just that, by using historical data to create a “calibration set” for each new prediction. This helps ensure that the predictions are accurate and don’t stray too far from reality. |
Keywords
* Artificial intelligence * Inference
