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Found 3 papers, 2 papers with code
A comparative study of conformal prediction methods for valid uncertainty quantification in machine learning
However, while people are still trying to improve the predictive power of their models, the community is starting to realize that for many applications it is not so much the exact prediction that is of importance, but rather the variability or uncertainty.
Conditional validity of heteroskedastic conformal regression
This paper tries to shed new light on how prediction intervals can be constructed, using methods such as normalized and Mondrian conformal prediction, in such a way that they adapt to the heteroskedasticity of the underlying process.
Valid prediction intervals for regression problems
Over the last few decades, various methods have been proposed for estimating prediction intervals in regression settings, including Bayesian methods, ensemble methods, direct interval estimation methods and conformal prediction methods.
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