Summary of From Conformal Predictions to Confidence Regions, by Charles Guille-escuret and Eugene Ndiaye
From Conformal Predictions to Confidence Regions
by Charles Guille-Escuret, Eugene Ndiaye
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); 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 The novel approach, called CCR, combines conformal prediction intervals for model outputs to establish confidence regions for model parameters, offering coverage guarantees under minimal assumptions on noise. This methodology is applicable to various predictive models, including linear models, which can be reduced to a Mixed-Integer Linear Program (MILP) for efficient computation of confidence intervals. The paper empirically compares CCR with recent advancements in challenging settings featuring heteroskedastic and non-Gaussian noise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to show how sure we are about the numbers that come from predictive models. This method, called CCR, works by combining two types of predictions: one for the model’s output and another for the model’s parameters. It can be used with many different types of models, including ones that are linear like a straight line. When used with linear models, this method can be simplified to make it easier to calculate confidence intervals. The paper shows how well CCR works in situations where there is noise in the data. |
