FastFeatureCP(FFCP)

Accelerating Feature Conformal Prediction via Taylor Approximation

Zihao Tang, Boyuan Wang, Chuan Wen, Jiaye Teng

News

10/08/2025

1. Our work is accepted at NeurIPS2025.

12/03/2024

1. Our paper is updated on arXiv.

Introduction

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 first-order 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 Split CP version) while achieving a significant reduction in computational time by approximately 50x in both regression and classification tasks.

• Feature Conformal Prediction (FCP) : Paper & Code

• Conformalized Quantile Regression (CQR) : Paper & Code

• Regularized Adaptive Prediction Sets (RAPS) : Paper & Code

• Localized Conformal Prediction (LCP) : Paper

Installation

We use Python 3.8, and other packages can be installed by:

pip install -r requirements.txt

Usage

Fast Feature Conformal Prediction (FFCP)

One-dim regression

cd FastFeatureCP
python main.py --data com

High-dimensional regression

Synthetic dataset:

cd FastFeatureCP
python main.py --data x100-y10-reg

Cityscapes

export $CITYSCAPES_PATH = 'your path to the cityscapes'
cd FastFeatureCP_seg
python main_fcn.py --dataset-dir $CITYSCAPES_PATH

Fast Feature Conformalized Quantile Regression (FFCQR)

cd FastFeatureCQR
python main_CQR.py --data com

Fast Feature Localized Conformal Prediction (FFLCP)

cd FastFeatureLCP
python main_LCP.py --data com

Fast Feature Regularized Adaptive Prediction Sets (FFRAPS)

export $imagenet_val_PATH = 'your path to the imagenet_val'
cd FastFeatureLCP
python main_FFRaps.py $imagenet_val_PATH

3. Citation

If you find our work is helpful to you, please cite our paper:

@article{tang2024predictive,
  title={Predictive Inference With Fast Feature Conformal Prediction},
  author={Tang, Zihao and Wang, Boyuan and Wen, Chuan and Teng, Jiaye},
  journal={arXiv preprint arXiv:2412.00653},
  year={2024}
}