Summary of Beyond the Norms: Detecting Prediction Errors in Regression Models, by Andres Altieri et al.
Beyond the Norms: Detecting Prediction Errors in Regression Models
by Andres Altieri, Marco Romanelli, Georg Pichler, Florence Alberge, Pablo Piantanida
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 tackles the challenge of detecting unreliable behavior in regression algorithms, which may arise from intrinsic variability or modeling errors. The authors formally introduce the notion of unreliability in regression, estimating the discrepancy density and measuring its statistical diversity using a proposed metric for statistical dissimilarity. They then derive a data-driven score that expresses the uncertainty of the regression outcome. The paper demonstrates empirical improvements in error detection for multiple regression tasks, outperforming popular baseline approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure machine learning algorithms don’t give you wrong answers. Sometimes these algorithms make mistakes because they’re not perfect or because there’s just random noise involved. The authors came up with a way to measure how likely it is that the algorithm will make a mistake, and then used this measurement to improve how well the algorithm can detect when it’s making a mistake. They tested their approach on several different tasks and found that it worked better than other methods people have tried. |
Keywords
» Artificial intelligence » Machine learning » Regression
