Accelerating Feature Conformal Prediction via Taylor Approximation
Zihao Tang, Boyuan Wang, Chuan Wen, Jiaye Teng
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 present Fast Feature Conformal Prediction (FFCP), a method that accelerates FCP by leveraging a Taylor expansion to approximate these non-linear operations.The proposed FFCP introduces a novel non-conformity score that is both effective and efficient for real-world applications.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 in both regression and classification tasks.