The V-fold jackknife for semiparametric inference: variance estimation, confidence intervals, and simultaneous confidence bands

What the math gives to ML

The paper provides a finite-fold uncertainty estimator that remains theoretically valid even when the estimator is produced by a complex machine-learning pipeline and its influence function is unavailable or unstable. Its key transferable asset is that only V leave-fold-out refits are required, while Studentization yields a t-like pivot with V-1 degrees of freedom even when the jackknife variance itself is not consistent for fixed V. This suggests a practical uncertainty wrapper for neural predictors, treatment-effect networks, survival models, or other costly estimators: use fold-refit predictions as pseudo-values and calibrate intervals from their empirical dispersion rather than from fragile Hessian or influence-function calculations. The method is more useful for reliable uncertainty and model comparison than for improving raw training accuracy.