Regression Prediction Averaging With Variance Quantification

* 🏆 1998: [Gradient-based learning applied to document recognition (CNN/GTN)](https://ieeexplore.ieee.org/abstract/document/726791)

Unique: Leverages bootstrap-induced prediction variance across ensemble members as a natural uncertainty quantification mechanism without requiring explicit probabilistic modeling or Bayesian inference — the variance of M predictions directly estimates prediction uncertainty, enabling confidence intervals from ensemble disagreement alone

vs others: Simpler than Bayesian regression or quantile regression for uncertainty estimation and more computationally efficient than Monte Carlo dropout, but provides only point-wise variance estimates rather than full predictive distributions