提出快速特征共形预测，计算速度提升50倍且保持精度
Predictive Inference With Fast Feature Conformal Prediction
- 用泰勒展开近似非线性变换，实现特征空间到输出空间的快速映射
- 在多个数据集上与原版特征共形预测性能相当，置信区间更窄
- 适合需要实时不确定性估计的部署场景，如自动驾驶、医疗诊断
共形预测因其后验、无需分布假设和模型无关的特性，在不确定性量化中被广泛应用。在现代深度学习领域，特征共形预测（FCP）通过在特征空间中应用共形预测，显著缩短了置信区间。然而，其实际应用受限于将置信区间从特征空间映射到输出空间所需的耗时非线性操作。本文提出快速特征共形预测（FFCP），引入新型非一致性评分，并利用泰勒展开近似该非线性过程，实现高效计算。实验表明，FFCP在多个基准数据集上的表现与原版FCP相当（均优于标准共形预测），同时计算时间减少约50倍。代码已开源：https://github.com/ElvisWang1111/FastFeatureCP
原文摘要 · Abstract (English)
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
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