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stat.ML2024
A comparative study of conformal prediction methods for valid uncertainty quantification in machine learning
Nicolas Dewolf
In the past decades, most work in the area of data analysis and machine learning was focused on optimizing predictive models and getting better results than what was possible with…
stat.ML2023★ 2 cited
Conditional validity of heteroskedastic conformal regression
Nicolas Dewolf, Bernard De Baets, Willem Waegeman
Conformal prediction, and split conformal prediction as a specific implementation, offer a distribution-free approach to estimating prediction intervals with statistical guarantees…
stat.ML2021★ 63 cited
Valid prediction intervals for regression problems
Nicolas Dewolf, Bernard De Baets, Willem Waegeman
Over the last few decades, various methods have been proposed for estimating prediction intervals in regression settings, including Bayesian methods, ensemble methods, direct inter…