Epistemic Uncertainty Estimation in Regression Ensemble Models with Pairwise Epistemic Estimators

Lucas Berry, David Meger

Keywords: Uncertainty Quantification, Dynamics Modeling, Ensembles

TL;DR: This work introduces a fast and efficient method for epistemic uncertainty in ensemble models for regression. Our method significantly improves baseline active learning methods on high-dimensional tasks.

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.

Primary Area: General machine learning (supervised, unsupervised, online, active, etc.)

Submission Number: 10662