Conformal prediction is widely adopted in uncertainty quantification, due to its post-hoc, distribution-free, and model-agnostic properties. In the realm of modern deep learning, researchers have proposed Feature Conformal Prediction (FCP), which deploys conformal prediction in a feature space, yielding reduced band lengths. However, the practical utility of FCP is limited due to the time-consuming non-linear operations required to transform confidence bands from feature space to output space. In this paper, we introduce Fast Feature Conformal Prediction (FFCP), which features a novel non-conformity score and is convenient for practical applications. FFCP serves as a fast version of FCP, in that it equivalently employs a Taylor expansion to approximate the aforementioned non-linear operations in FCP. Empirical validations showcase that FFCP performs comparably with FCP (both outperforming the vanilla version) while achieving a significant reduction in computational time by approximately 50x. The code is available at https://github.com/ElvisWang1111/FastFeatureCP

翻译：保形预测因其后处理、无分布和模型无关的特性，在不确定性量化领域得到广泛应用。在现代深度学习的背景下，研究者提出了特征保形预测（FCP），该方法在特征空间中部署保形预测，从而获得更窄的置信带。然而，由于将置信带从特征空间转换到输出空间需要耗时的非线性操作，FCP的实际应用受到限制。本文提出快速特征保形预测（FFCP），该方法采用一种新颖的非符合性评分，便于实际应用。FFCP作为FCP的快速版本，通过等价地使用泰勒展开来近似FCP中的上述非线性操作。实证验证表明，FFCP在性能上与FCP相当（两者均优于原始版本），同时计算时间显著减少约50倍。代码可在 https://github.com/ElvisWang1111/FastFeatureCP 获取。