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

Escaping the Sample Trap: Fast and Accurate Epistemic Uncertainty  Estimation with Pairwise-Distance Estimators

Design of de novo biological sequences with desired properties, like protein
and DNA sequences, often involves an active loop with several rounds of
molecule ideation and expensive wet-lab evaluations. These experiments can
consist of multiple stages, with increasing levels of precision and cost of
evaluation, where candidates are filtered. This makes the diversity of proposed
candidates a key consideration in the ideation phase. In this work, we propose
an active learning algorithm leveraging epistemic uncertainty estimation and
the recently proposed GFlowNets as a generator of diverse candidate solutions,
with the objective to obtain a diverse batch of useful (as defined by some
utility function, for example, the predicted anti-microbial activity of a
peptide) and informative candidates after each round. We also propose a scheme
to incorporate existing labeled datasets of candidates, in addition to a reward
function, to speed up learning in GFlowNets. We present empirical results on
several biological sequence design tasks, and we find that our method generates
more diverse and novel batches with high scoring candidates compared to
existing approaches.

本文提出一种利用表观不确定性评估和最近提出的 GFlowNets 作为生成器的主动学习算法，旨在在每一轮后获得一批有用且信息量大的候选解，并在此基础上设计了新的生物序列设计任务模型，并对其进行了实验验证，在多个任务上取得了良好的效果。