Conformal novelty detection with false discovery rate control at the boundary
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915858366857216 |
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| author | Gao, Zijun Roquain, Etienne Xiang, Daniel |
| author_facet | Gao, Zijun Roquain, Etienne Xiang, Daniel |
| contents | Conformal novelty detection is a classical machine learning task for which uncertainty quantification is essential for providing reliable results. Recent work has shown that the BH procedure applied to conformal p-values controls the false discovery rate (FDR). Unfortunately, the BH procedure can lead to over-optimistic assessments near the rejection threshold, with an increase of false discoveries at the margin as pointed out by Soloff et al. (2024). This issue is solved therein by the support line (SL) correction, which is proven to control the boundary false discovery rate (bFDR) in the independent, non-conformal setting. The present work extends the SL method to the conformal setting: first, we show that the SL procedure can violate the bFDR control in this specific setting. Second, we propose several alternatives that provably control the bFDR in the conformal setting. Finally, numerical experiments with both synthetic and real data support our theoretical findings and show the relevance of the new proposed procedures. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_02610 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Conformal novelty detection with false discovery rate control at the boundary Gao, Zijun Roquain, Etienne Xiang, Daniel Methodology Machine Learning Conformal novelty detection is a classical machine learning task for which uncertainty quantification is essential for providing reliable results. Recent work has shown that the BH procedure applied to conformal p-values controls the false discovery rate (FDR). Unfortunately, the BH procedure can lead to over-optimistic assessments near the rejection threshold, with an increase of false discoveries at the margin as pointed out by Soloff et al. (2024). This issue is solved therein by the support line (SL) correction, which is proven to control the boundary false discovery rate (bFDR) in the independent, non-conformal setting. The present work extends the SL method to the conformal setting: first, we show that the SL procedure can violate the bFDR control in this specific setting. Second, we propose several alternatives that provably control the bFDR in the conformal setting. Finally, numerical experiments with both synthetic and real data support our theoretical findings and show the relevance of the new proposed procedures. |
| title | Conformal novelty detection with false discovery rate control at the boundary |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2601.02610 |