This work introduces a novel approach for epistemic uncertainty estimation for ensemble models using pairwise-distance estimators (PaiDEs). These estimators utilize the pairwise-distance between model components to establish bounds on entropy and uses said bounds as estimates for information-based criterion. Unlike recent deep learning methods for epistemic uncertainty estimation, which rely on sample-based Monte Carlo estimators, PaiDEs are able to estimate epistemic uncertainty up to 100$\times$ faster, over a larger space (up to 100$\times$) and perform more accurately in higher dimensions. To validate our approach, we conducted a series of experiments commonly used to evaluate epistemic uncertainty estimation: 1D sinusoidal data, Pendulum-v0, Hopper-v2, Ant-v2 and Humanoid-v2. For each experimental setting, an Active Learning framework was applied to demonstrate the advantages of PaiDEs for epistemic uncertainty estimation.

该研究引入了一种新颖的方法，使用成对距离评估器（PaiDEs）对集成模型进行认识不确定性估计，这些评估器利用模型组件之间的成对距离建立熵的上下界，并使用这些界限作为信息准则的估计值。与最近的深度学习方法不同，这些方法依赖于基于样本的蒙特卡洛估计器，PaiDEs能够以高达100倍的速度，覆盖更大空间（高达100倍），在更高维度上实现更准确的认识不确定性估计。为了验证我们的方法，我们进行了一系列常用于评估认识不确定性估计的实验：1D正弦数据，Pendulum-v0，Hopper-v2，Ant-v2和Humanoid-v2。基于每个实验环境，应用主动学习框架来展示PaiDEs在认识不确定性估计方面的优势。

逃脱样本陷阱：使用配对距离估计器快速精确的认知不确定度估计