Class-Conditional Conformal Prediction with Many Classes
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Abstract (arXiv)
Standard conformal prediction methods provide a marginal coverage guarantee, which means that for a random test point, the conformal prediction set contains the true label with a user-specified probability. In many classification problems, we would like to obtain a stronger guarantee--that for test points of a specific class, the prediction set contains the true label with the same user-chosen probability. For the latter goal, existing conformal prediction methods do not work well when there is a limited amount of labeled data per class, as is often the case in real applications where the number of classes is large. We propose a method called clustered conformal prediction that clusters together classes having "similar" conformal scores and performs conformal prediction at the cluster level. Based on empirical evaluation across four image data sets with many (up to 1000) classes, we find that clustered conformal typically outperforms existing methods in terms of class-conditional coverage and set size metrics.
Key facts. Cited by 109 papers in the map (data through 7 Oct 2026). Builds on 15 of its 29 references that are in the map. First posted on arXiv 15 Jun 2023.
Builds on
15 of 29 references.
References · 15
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- 2019Conformalized Quantile Regressioncited by 620
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Citing papers per year: 2023: 3; 2024: 25; 2025: 39; 2026: 42.
109 papers.
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- 2026Does Marginal Coverage Guarantee Class-Conditional Safety for Zero-Shot VLMs Under Shift?19 Aug 2026
- 2026LATA: Laplacian-Assisted Transductive Adaptation for Conformal Uncertainty in Medical VLMs19 Feb 2026
- 2025Trustworthy Few-Shot Transfer of Medical VLMs through Split Conformal Prediction20 Jun 2025
- 2025Full Conformal Adaptation of Medical Vision-Language Models6 Jun 2025
- 2025Conformal Prediction for Zero-Shot Models30 May 2025
- 2024Are foundation models for computer vision good conformal predictors?8 Dec 2024
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- 2024Conformal Prediction: A Data Perspectivecited by 25
- 2024Conformal Prediction with Learned Featurescited by 23
- 2024Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibrationcited by 18
- 2025Rectifying Conformity Scores for Better Conditional Coveragecited by 17
- 2024Self-Calibrating Conformal Predictioncited by 16
- 2023Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithmscited by 14
- 2024Conformal Prediction Sets Can Cause Disparate Impactcited by 14
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- 2026PICPIs: Prediction-Interval-Conditional Prediction Intervals21 Sep 2026
- 2026Calibration Count Reuse: Validity Does Not Determine Efficiency21 Sep 2026
- 2026Multi-source conformal prediction: leveraging heterogeneity via localization13 Sep 2026
- 2026Improving the Sensitivity of Gravitational Wave Detection with Weighted Conformal Prediction10 Sep 2026
- 2026From Uncertainty to Clinical Risk: Severity-Aware Conformal Planning for Interactive Medical Diagnosis28 Aug 2026
- 2026The Exceedance Design Effect: Effective Sample Size for Thresholds under Clustering21 Aug 2026
- 2026Temporal Graph Prototype-conditioned Conformal Prediction for Fraud Detection16 Aug 2026
- 2026Regime-Conditional Verification: Correctness Estimation for Adapting and Monitoring Safety Classifiers14 Aug 2026
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