Summary of Regression Conformal Prediction Under Bias, by Matt Y. Cheung et al.
Regression Conformal Prediction under Bias
by Matt Y. Cheung, Tucker J. Netherton, Laurence E. Court, Ashok Veeraraghavan, Guha Balakrishnan
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Statistics Theory (math.ST); 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 Uncertainty quantification is essential in high-impact applications, where machine learning algorithms make imperfect predictions. Conformal prediction (CP) is a powerful framework that generates calibrated prediction intervals with valid coverage. This work investigates how bias affects CP intervals, particularly in regression tasks. We analyze the influence of symmetric and asymmetric adjustments on interval lengths. Our theoretical and empirical analyses show that symmetrically adjusted interval lengths increase by 2|b| where b is the globally applied scalar value representing bias. In contrast, asymmetrically adjusted interval lengths are not affected by bias. We demonstrate our results using two real-world prediction tasks: sparse-view computed tomography (CT) reconstruction and time-series weather forecasting. Our work paves the way for more bias-robust machine learning systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making better predictions in machine learning. When machines make mistakes, it’s important to know how wrong they are. The researchers studied a method called conformal prediction that helps with this. They found out that when the predictions are biased (meaning they’re not accurate), this method gets worse at giving accurate intervals. But they also discovered a new way to adjust these intervals that is more flexible and can handle bias better. This means that in the future, we might have machine learning systems that are less affected by mistakes. |
Keywords
» Artificial intelligence » Machine learning » Regression » Time series
