Summary of Conformal Thresholded Intervals For Efficient Regression, by Rui Luo and Zhixin Zhou
Conformal Thresholded Intervals for Efficient Regression
by Rui Luo, Zhixin Zhou
First submitted to arxiv on: 19 Jul 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 A novel conformal regression method called Conformal Thresholded Intervals (CTI) is introduced, aiming to produce the smallest possible prediction set with guaranteed coverage. Unlike existing methods, CTI estimates conditional probability densities using off-the-shelf multi-output quantile regression, leveraging the inverse relationship between interval length and probability density. The optimal threshold is determined through calibration, balancing size and coverage. CTI achieves superior performance compared to state-of-the-art conformal regression methods across various datasets, producing smaller prediction sets while maintaining desired coverage levels. This method offers a simple yet effective solution for reliable uncertainty quantification in regression tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Conformal Thresholded Intervals (CTI) is a new way to predict things correctly. It helps make sure that the predictions are correct by making sure they fall within certain ranges. This works better than other methods because it uses special formulas and calculations to get the right results. The method is tested on many different datasets and does really well, making smaller predictions while still being accurate. |
Keywords
* Artificial intelligence * Probability * Regression
