Summary of Conformal Approach to Gaussian Process Surrogate Evaluation with Coverage Guarantees, by Edgar Jaber (edf R&d Prisme et al.
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees
by Edgar Jaber, Vincent Blot, Nicolas Brunel, Vincent Chabridon, Emmanuel Remy, Bertrand Iooss, Didier Lucor, Mathilde Mougeot, Alessandro Leite
First submitted to arxiv on: 15 Jan 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 proposed method combines Gaussian processes with conformal prediction to build adaptive cross-conformal prediction intervals that are robust and model-agnostic. By weighting the non-conformity score with the posterior standard deviation of the GP, the resulting intervals exhibit adaptivity similar to Bayesian credibility sets while avoiding underlying assumptions and providing frequentist coverage guarantees. This method can be used to evaluate the quality of a GP surrogate model and inform prior selection for specific applications. The approach is demonstrated through numerical examples based on various reference databases and has potential applicability in the context of surrogate modeling, as illustrated by its application to a clogging phenomenon simulator. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gaussian processes are a type of machine learning that helps us understand uncertainty. They’re often used to predict how things will behave in complex simulations. But there’s a problem: these predictions assume certain things about the world, and those assumptions might not always be true. To fix this, scientists have developed a new way to create prediction intervals that work regardless of what’s going on in the simulation. This method is like a special kind of insurance policy for our predictions. It helps us understand how good or bad our predictions are and can even help us choose the best approach for a specific problem. |
Keywords
* Artificial intelligence * Machine learning
