Conformal Novelty Detection via Data-enhanced Localization
Abstract
Novelty detection is an important area of research in both statistics and machine learning. In this paper, we focus on the conditional novelty detection problem, where novelties arise from the relationship between different variables. We first adopt the conformal inference framework and propose a data-enhanced localized conformal p-value, constructed by recalibrating an enhanced conditional distribution estimator. This estimator efficiently captures conditional information by incorporating both calibration and test data into its kernel estimation. We show that our p-values are valid in finite samples and can improve detection efficiency. Based on this p-value, we then develop a novel conditional novelty detection algorithm, along with a data-driven bandwidth selection method that ensures finite-sample false discovery rate control while enhancing detection power. Both simulated and real data experiments demonstrate the advantages of our proposed approach.
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BibTeX
@misc{wu2026conformal,
title = {{Conformal Novelty Detection via Data-enhanced Localization}},
author = {Xiaoyang Wu and Jiajun Liu and Haojie Ren and Changliang Zou},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.34999},
eprint = {2610.34999},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.34999},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Conformal Novelty Detection via Data-enhanced Localization
AU - Xiaoyang Wu
AU - Jiajun Liu
AU - Haojie Ren
AU - Changliang Zou
PY - 2026
DA - 2026/10/26
PB - arXiv
DO - 10.48550/arxiv.2610.34999
UR - https://arxiv.org/abs/2610.34999
AB - Novelty detection is an important area of research in both statistics and machine learning. In this paper, we focus on the conditional novelty detection problem, where novelties arise from the relationship between different variables. We first adopt the conformal inference framework and propose a data-enhanced localized conformal p-value, constructed by recalibrating an enhanced conditional distribution estimator. This estimator efficiently captures conditional information by incorporating both calibration and test data into its kernel estimation. We show that our p-values are valid in finite samples and can improve detection efficiency. Based on this p-value, we then develop a novel conditional novelty detection algorithm, along with a data-driven bandwidth selection method that ensures finite-sample false discovery rate control while enhancing detection power. Both simulated and real data experiments demonstrate the advantages of our proposed approach.
ER -