Summary of Conformal Prediction Via Regression-as-classification, by Etash Guha et al.
Conformal Prediction via Regression-as-Classification
by Etash Guha, Shlok Natarajan, Thomas Möllenhoff, Mohammad Emtiyaz Khan, Eugene Ndiaye
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 addresses challenges in conformal prediction (CP) for regression tasks with complex output distributions. The authors recognize issues with existing approaches, such as estimation errors and unstable intervals. To overcome these limitations, they propose a novel method that converts regression to classification, leveraging CP for classification to obtain reliable interval estimates. A new loss function is designed to preserve the ordering of the continuous-output space. Experimental results on various benchmarks demonstrate the effectiveness of this simple yet powerful approach in handling practical problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem with predicting outcomes when things don’t always follow a normal pattern. Right now, some methods can be tricky because they’re sensitive to small mistakes and produce unpredictable results. To fix this, researchers came up with a new way to turn regression tasks into classification problems and use conformal prediction for classification. This approach ensures the predicted intervals make sense in the continuous output space. The results show that this simple solution works well on many real-world problems. |
Keywords
» Artificial intelligence » Classification » Loss function » Regression
