Symbolic Quantile Regression for the Interpretable Prediction of Conditional Quantiles

ArXi:2508.08080v2 Announce Type: replace Symbolic Regression (SR) is a well-established framework for generating interpretable or white-box predictive models. Although SR has been successfully applied to create interpretable estimates of the average of the outcome, it is currently not well understood how it can be used to estimate the relationship between variables at other points in the distribution of the target variable. Such estimates of e.g.