mlsanityData to 7 Oct 2026

Classification with Valid and Adaptive Coverage

arXiv 2006.02544 ↗First posted 3 Jun 2020stat.ME stat.MLcited by 390

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Abstract (arXiv)

Conformal inference, cross-validation+, and the jackknife+ are hold-out methods that can be combined with virtually any machine learning algorithm to construct prediction sets with guaranteed marginal coverage. In this paper, we develop specialized versions of these techniques for categorical and unordered response labels that, in addition to providing marginal coverage, are also fully adaptive to complex data distributions, in the sense that they perform favorably in terms of approximate conditional coverage compared to alternative methods. The heart of our contribution is a novel conformity score, which we explicitly demonstrate to be powerful and intuitive for classification problems, but whose underlying principle is potentially far more general. Experiments on synthetic and real data demonstrate the practical value of our theoretical guarantees, as well as the statistical advantages of the proposed methods over the existing alternatives.

Key facts. Cited by 390 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 37; 2023: 45; 2024: 94; 2025: 116; 2026: 81.

390 papers.

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  1. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 777
  2. 2021Adaptive Conformal Inference Under Distribution Shiftcited by 360
  3. 2022Conformal Risk Controlcited by 290
  4. 2021Distribution-Free, Risk-Controlling Prediction Setscited by 278
  5. 2021Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Controlcited by 256
  6. 2021Testing for Outliers with Conformal p-valuescited by 133
  7. 2024Benchmarking LLMs via Uncertainty Quantificationcited by 124
  8. 2023Class-Conditional Conformal Prediction with Many Classescited by 109
  9. 2021Learning Optimal Conformal Classifierscited by 108
  10. 2022Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imagingcited by 107
  11. 2021Distribution-free uncertainty quantification for classification under label shiftcited by 99
  12. 2023Uncertainty Quantification over Graph with Conformalized Graph Neural Networkscited by 79
38 more
  1. 2021Conformal Prediction using Conditional Histogramscited by 77
  2. 2023Improved Online Conformal Prediction via Strongly Adaptive Online Learningcited by 76
  3. 2021Fair Conformal Predictors for Applications in Medical Imagingcited by 74
  4. 2022Sequential Predictive Conformal Inference for Time Seriescited by 71
  5. 2022Batch Multivalid Conformal Predictioncited by 66
  6. 2022Practical Adversarial Multivalid Conformal Predictioncited by 65
  7. 2022Training Uncertainty-Aware Classifiers with Conformalized Deep Learningcited by 62
  8. 2022Improving Expert Predictions with Conformal Predictioncited by 54
  9. 2021Conformalized Survival Analysiscited by 53
  10. 2023Conformal Prediction for Deep Classifier via Label Rankingcited by 52
  11. 2022Probabilistic Conformal Prediction Using Conditional Random Samplescited by 47
  12. 2023Federated Conformal Predictors for Distributed Uncertainty Quantificationcited by 46
  13. 2024Length Optimization in Conformal Predictioncited by 39
  14. 2021Tracking the risk of a deployed model and detecting harmful distribution shiftscited by 38
  15. 2022Achieving Risk Control in Online Learning Settingscited by 38
  16. 2022Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Setscited by 38
  17. 2024Conformal prediction for multi-dimensional time series by ellipsoidal setscited by 37
  18. 2025Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agentscited by 37
  19. 2024Conformal Prediction Sets Improve Human Decision Makingcited by 35
  20. 2022On the Utility of Prediction Sets in Human-AI Teamscited by 34
  21. 2021Calibrated and Sharp Uncertainties in Deep Learning via Density Estimationcited by 33
  22. 2022Predictive Inference with Feature Conformal Predictioncited by 33
  23. 2023Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty Propagationcited by 33
  24. 2023Conformal Nucleus Samplingcited by 32
  25. 2023Conformal prediction under ambiguous ground truthcited by 31
  26. 2023Conformal Prediction for Federated Uncertainty Quantification Under Label Shiftcited by 29
  27. 2022Conformal Prediction Sets with Limited False Positivescited by 28
  28. 2022Distribution Free Prediction Sets for Node Classificationcited by 28
  29. 2024Uncertainty-Aware Evaluation for Vision-Language Modelscited by 28
  30. 2021Set-valued classification -- overview via a unified frameworkcited by 27
  31. 2024Provably Robust Conformal Prediction with Improved Efficiencycited by 27
  32. 2025Analyzing Uncertainty of LLM-as-a-Judge: Interval Evaluations with Conformal Predictioncited by 26
  33. 2022Efficient and Differentiable Conformal Prediction with General Function Classescited by 25
  34. 2023Beyond Confidence: Reliable Models Should Also Consider Atypicalitycited by 25
  35. 2023Probabilistically robust conformal predictioncited by 25
  36. 2024Towards Human-AI Complementarity with Prediction Setscited by 25
  37. 2024Conformal Prediction: A Data Perspectivecited by 25
  38. 2023Adaptive conformal classification with noisy labelscited by 24

50 most cited of 390 All 390 (JSON)