Regression With Continuous Target Prediction And Uncertainty Quantification

* 🏆 2001: [A fast and elitist multiobjective genetic algorithm (NSGA-II)](https://ieeexplore.ieee.org/abstract/document/996017)

Unique: Provides built-in prediction intervals by computing the standard deviation of predictions across trees, avoiding the need for separate uncertainty quantification methods like quantile regression or Bayesian approaches — this is computationally efficient and naturally captures model uncertainty from ensemble variance

vs others: Faster and simpler than gradient boosting for regression (no learning rate tuning) and more interpretable than neural networks, while providing uncertainty estimates that are more practical than Bayesian methods for practitioners without probabilistic modeling expertise

via “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