分类任务中的成对差分学习不确定性量化方法
Uncertainty Quantification in Pairwise Difference Learning for Classification
arXiv 上一篇方法论论文，把成对差分学习搬进分类任务，还给了量化预测不确定性的一整套做法，做分类模型的同学可以看看。
论文将成对差分学习（PDL）扩展到分类任务，核心思路是训练一个接收两个实例、预测结果差异的函数，再对所有训练样本的预测取平均得到查询实例的输出。作者提出了一种适配该范式的元学习技术。针对不确定性量化问题，论文基于将总不确定性分解为偶然不确定性与认知不确定性的最新方法，按底层基学习器表示不确定性的方式提出了多种扩展方案。大规模实证研究同时评估了 PDL 分类的预测准确率与不确定性感知能力。
Uncertainty Quantification in Pairwise Difference Learning for Classification
Instead of learning a mapping from instances to outcomes in the standard way, the key idea of pairwise difference learning (PDL) is to learn a function that takes two instances as input and predicts the difference between the respective outcomes. Given a function of this kind, predictions for a query instance are derived from every training example and then averaged. We propose a version of PDL for the task of classification and propose a suitable meta-learning technique for this purpose. Moreover, we address the problem of uncertainty quantification, i.e., of representing and quantifying the uncertainty of a PDL classifier's predictions in a faithful manner. Building on recent work on the decomposition of total uncertainty into aleatoric and epistemic components, we propose different ways of extending corresponding measures to the setting of PDL, depending on how and to what extent uncertainty is represented by the underlying base learner. In large-scale empirical studies, we analyze both the predictive accuracy and the uncertainty awareness of our PDL classifier.
