Epistemic Uncertainty Estimation in Regression Ensemble Models with Pairwise Epistemic Estimators

Abstract

This work introduces a novel approach, Pairwise Epistemic Estimators (PairEpEsts), for epistemic uncertainty estimation in ensemble models for regression tasks using pairwise-distance estimators (PaiDEs). By utilizing the pairwise distances between model components, PaiDEs establish bounds on entropy. We leverage this capability to enhance the performance of Bayesian Active Learning by Disagreement (BALD). Notably, unlike sample-based Monte Carlo estimators, PairEpEsts can estimate epistemic uncertainty up to 100 times faster and demonstrate superior performance in higher dimensions. To validate our approach, we conducted a varied series of regression experiments on commonly used benchmarks: 1D sinusoidal data, *Pendulum*, *Hopper*, *Ant*, and *Humanoid*, demonstrating PairEpEsts’ advantage over baselines in high-dimensional regression active learning.

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Cite

Text

Berry and Meger. "Epistemic Uncertainty Estimation in Regression Ensemble Models with Pairwise Epistemic Estimators." Advances in Neural Information Processing Systems, 2025.

Markdown

[Berry and Meger. "Epistemic Uncertainty Estimation in Regression Ensemble Models with Pairwise Epistemic Estimators." Advances in Neural Information Processing Systems, 2025.](https://mlanthology.org/neurips/2025/berry2025neurips-epistemic/)

BibTeX

@inproceedings{berry2025neurips-epistemic,
  title     = {{Epistemic Uncertainty Estimation in Regression Ensemble Models with Pairwise Epistemic Estimators}},
  author    = {Berry, Lucas and Meger, David},
  booktitle = {Advances in Neural Information Processing Systems},
  year      = {2025},
  url       = {https://mlanthology.org/neurips/2025/berry2025neurips-epistemic/}
}