Summary of Conformal Prediction For Class-wise Coverage Via Augmented Label Rank Calibration, by Yuanjie Shi et al.
Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration
by Yuanjie Shi, Subhankar Ghosh, Taha Belkhouja, Janardhan Rao Doppa, Yan Yan
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 new algorithm for conformal prediction (CP) called Rank Calibrated Class-conditional CP (RC3P), which addresses the issue of large prediction sets produced by traditional CP methods, particularly in imbalanced classification tasks. RC3P reduces the prediction set sizes while maintaining class-wise coverage guarantees. The algorithm iteratively thresholds the conformity score for a subset of classes with low top-k error rates, allowing it to selectively apply the thresholding process only where necessary. This approach achieves agnostic class-wise coverage and reduces prediction set sizes by 26.25% on average compared to traditional CP methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces an algorithm called Rank Calibrated Class-conditional CP (RC3P) that helps with making predictions in situations where we’re not sure what will happen. Right now, there are some problems with the way we do this, and RC3P is a new approach to fix these issues. It’s especially helpful when dealing with lots of different classes or categories, like in image recognition tasks. The algorithm works by looking at how well a model does on each class and adjusting its predictions based on that. This helps us make more accurate predictions while also being able to provide some uncertainty about our results. |
Keywords
» Artificial intelligence » Classification
