Adaptive conformal inference is a highly flexible framework for constructing uncertainty sets with a valid coverage guarantee in an online setting, in which the underlying data distribution can drastically -- and even adversarially -- shift over time. In this report, we propose an instantiation of ACI that can be easily integrated with any online learning algorithm, requiring minimal implementation effort and computational cost. Additionally, we provide approaches for constructing intervals that quickly adapt to new changes in the distribution. Using synthetic and real-world benchmark data sets, we demonstrate the improved performance of our proposal over existing techniques.

翻译：适应性一致推断是一个非常灵活的框架,用于构建不确定性数据集,在网上环境中提供有效的覆盖保障,其中基础数据分布可随时间而急剧变化,甚至对抗性变化。我们在本报告中建议立即采用ACI系统,该系统可以很容易地与任何在线学习算法相结合,需要最低限度的实施努力和计算成本。此外,我们提供了构建间隔的方法,以便迅速适应分布中的新变化。我们使用合成和现实世界基准数据集,展示了我们的提案对现有技术的更好表现。