Summary of Relaxed Quantile Regression: Prediction Intervals For Asymmetric Noise, by Thomas Pouplin et al.
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise
by Thomas Pouplin, Alan Jeffares, Nabeel Seedat, Mihaela van der Schaar
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 paper proposes a new approach to constructing prediction intervals, which is crucial for high-stakes decisions where uncertainty quantification matters. Existing methods like quantile regression are simple, interpretable, and effective but have limitations, such as requiring arbitrary choices of specific quantiles or learning excessive numbers of intervals. The proposed Relaxed Quantile Regression (RQR) method removes these constraints while maintaining the strengths of quantile regression. RQR achieves improved interval qualities, such as mean width, while retaining essential coverage guarantees. This work addresses real-world applications where simple point predictions are insufficient for decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making better predictions by showing a range of possible answers instead of just one number. This is important because sometimes we need to know not just what might happen but also how likely it is to happen. The current way of doing this, called quantile regression, has some limitations. The new method proposed in this paper solves these problems and produces better results. |
Keywords
» Artificial intelligence » Regression
