论文标题
PAC预测集用于元学习
PAC Prediction Sets for Meta-Learning
论文作者
论文摘要
不确定性量化是针对安全至关重要系统（例如医疗或自动驾驶汽车）的机器学习模型的关键组成部分。我们在元学习的背景下研究了这个问题，目标是快速使预测因子适应新任务。特别是，我们提出了一种新颖的算法来构建\ emph {pac预测集}，该算法通过一组标签捕获不确定性，该标签只能通过仅几个培训示例来适应新任务。这些预测设置满足了典型的PAC保证对元学习设置的扩展；特别是，PAC保证在将来的任务上具有很高的可能性。我们证明了在三个应用程序域中的四个数据集上的方法的功效：视觉域中的Mini-ImageNet和Cifar10-C，语言域中的很少的Min-ImageNet和Medical域中的CDC Heart数据集。特别是，与其他满足此保证的基线相比，我们的预测设置满足PAC保证，同时具有较小的大小。
Uncertainty quantification is a key component of machine learning models targeted at safety-critical systems such as in healthcare or autonomous vehicles. We study this problem in the context of meta learning, where the goal is to quickly adapt a predictor to new tasks. In particular, we propose a novel algorithm to construct \emph{PAC prediction sets}, which capture uncertainty via sets of labels, that can be adapted to new tasks with only a few training examples. These prediction sets satisfy an extension of the typical PAC guarantee to the meta learning setting; in particular, the PAC guarantee holds with high probability over future tasks. We demonstrate the efficacy of our approach on four datasets across three application domains: mini-ImageNet and CIFAR10-C in the visual domain, FewRel in the language domain, and the CDC Heart Dataset in the medical domain. In particular, our prediction sets satisfy the PAC guarantee while having smaller size compared to other baselines that also satisfy this guarantee.
