Summary of A Conformal Prediction Score That Is Robust to Label Noise, by Coby Penso and Jacob Goldberger
A Conformal Prediction Score that is Robust to Label Noise
by Coby Penso, Jacob Goldberger
First submitted to arxiv on: 4 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 study proposes a conformal prediction (CP) calibration method for tackling noisy labels in validation sets. The approach introduces a robust conformal score that estimates the noise-free score using noisy labeled data and the noise level. This score is then used to form the prediction set, which is applied to several standard medical imaging classification datasets. The proposed algorithm outperforms current methods by a large margin in terms of average prediction set size while maintaining required coverage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study helps make computer predictions more reliable by fixing a problem with noisy labels. Noisy labels happen when data is incorrect or incomplete, and can mess up how well a model predicts things. The researchers developed a new way to calculate the uncertainty of a model’s predictions, which makes their predictions more trustworthy. |
Keywords
» Artificial intelligence » Classification
