用可能性理论更精准地表示认知不确定性，提升主动学习效果。
Improving Active Learning with a Bayesian Representation of Epistemic Uncertainty
- 结合概率与可能性理论，专门建模认知不确定性
- 在真实与模拟数据上均显著优于传统方法
- 适合需要高效标注的机器学习场景
主动学习中常通过减少认知不确定性来提升性能，而偶然不确定性通常被视为系统固有属性，难以降低。然而，区分两者仍具挑战，且尚无统一最优策略。本文提出一种概率与可能性理论的结合方法，利用可能性理论专门刻画认知不确定性，并由此推导出具备优良性质的新主动学习策略。为验证其在复杂场景中的有效性，引入可能性高斯过程（possibilistic Gaussian process），并应用于基于高斯过程的多分类与二分类任务，在模拟和真实数据集上均表现出优异性能。
原文摘要 · Abstract (English)
A popular strategy for active learning is to specifically target a reduction in epistemic uncertainty, since aleatoric uncertainty is often considered as being intrinsic to the system of interest and therefore not reducible. Yet, distinguishing these two types of uncertainty remains challenging and there is no single strategy that consistently outperforms the others. We propose to use a particular combination of probability and possibility theories, with the aim of using the latter to specifically represent epistemic uncertainty, and we show how this combination leads to new active learning strategies that have desirable properties. In order to demonstrate the efficiency of these strategies in non-trivial settings, we introduce the notion of a possibilistic Gaussian process (GP) and consider GP-based multiclass and binary classification problems, for which the proposed methods display a strong performance for both simulated and real datasets.
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