mlsanityData to 7 Oct 2026

Conformal prediction beyond exchangeability

arXiv 2202.13415 ↗First posted 27 Feb 2022stat.MEcited by 348

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Abstract (arXiv)

Conformal prediction is a popular, modern technique for providing valid predictive inference for arbitrary machine learning models. Its validity relies on the assumptions of exchangeability of the data, and symmetry of the given model fitting algorithm as a function of the data. However, exchangeability is often violated when predictive models are deployed in practice. For example, if the data distribution drifts over time, then the data points are no longer exchangeable; moreover, in such settings, we might want to use a nonsymmetric algorithm that treats recent observations as more relevant. This paper generalizes conformal prediction to deal with both aspects: we employ weighted quantiles to introduce robustness against distribution drift, and design a new randomization technique to allow for algorithms that do not treat data points symmetrically. Our new methods are provably robust, with substantially less loss of coverage when exchangeability is violated due to distribution drift or other challenging features of real data, while also achieving the same coverage guarantees as existing conformal prediction methods if the data points are in fact exchangeable. We demonstrate the practical utility of these new tools with simulations and real-data experiments on electricity and election forecasting.

Key facts. Cited by 348 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2022.

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Citing papers per year: 2022: 19; 2023: 41; 2024: 64; 2025: 98; 2026: 126.

348 papers.

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  1. 2022Conformal Risk Controlcited by 290
  2. 2024Language Models with Conformal Factuality Guaranteescited by 128
  3. 2022Conformal Inference for Online Prediction with Arbitrary Distribution Shiftscited by 123
  4. 2023Uncertainty Quantification over Graph with Conformalized Graph Neural Networkscited by 79
  5. 2023Improved Online Conformal Prediction via Strongly Adaptive Online Learningcited by 76
  6. 2022Sequential Predictive Conformal Inference for Time Seriescited by 71
  7. 2022Training Uncertainty-Aware Classifiers with Conformalized Deep Learningcited by 62
  8. 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 62
  9. 2024Online conformal prediction with decaying step sizescited by 53
  10. 2024Conformal Prediction for Natural Language Processing: A Surveycited by 52
  11. 2023How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Controlcited by 50
  12. 2023Credal Bayesian Deep Learningcited by 45
38 more
  1. 2023Conformal Prediction for Time Series with Modern Hopfield Networkscited by 40
  2. 2024Conformal prediction for multi-dimensional time series by ellipsoidal setscited by 37
  3. 2025LLM-as-a-Prophet: Understanding Predictive Intelligence with Prophet Arenacited by 36
  4. 2022Predictive Inference with Feature Conformal Predictioncited by 33
  5. 2023Conformal Prediction for Federated Uncertainty Quantification Under Label Shiftcited by 29
  6. 2023Non-Exchangeable Conformal Risk Controlcited by 29
  7. 2022Distribution Free Prediction Sets for Node Classificationcited by 28
  8. 2024Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)cited by 26
  9. 2024Conformal Prediction: A Data Perspectivecited by 25
  10. 2023Conformal Prediction with Missing Valuescited by 24
  11. 2023Adaptive conformal classification with noisy labelscited by 24
  12. 2023Conformal Meta-learners for Predictive Inference of Individual Treatment Effectscited by 23
  13. 2025Multivariate Conformal Prediction using Optimal Transportcited by 21
  14. 2024Uncertainty in Graph Neural Networks: A Surveycited by 20
  15. 2022Integrative conformal p-values for powerful out-of-distribution testing with labeled outlierscited by 19
  16. 2024Conformal Prediction for Causal Effects of Continuous Treatmentscited by 19
  17. 2025Reliable Text-to-SQL with Adaptive Abstentioncited by 19
  18. 2024Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibrationcited by 18
  19. 2023Adaptive Conformal Prediction by Reweighting Nonconformity Scorecited by 16
  20. 2022JAWS: Auditing Predictive Uncertainty Under Covariate Shiftcited by 14
  21. 2023Conformal inference is (almost) free for neural networks trained with early stoppingcited by 14
  22. 2023Conformal Prediction for Network-Assisted Regressioncited by 14
  23. 2025Wasserstein-regularized Conformal Prediction under General Distribution Shiftcited by 14
  24. 2023On the Expected Size of Conformal Prediction Setscited by 13
  25. 2024On Temperature Scaling and Conformal Prediction of Deep Classifierscited by 13
  26. 2025WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingalescited by 13
  27. 2024Discounted Adaptive Online Learning: Towards Better Regularizationcited by 12
  28. 2024Bellman Conformal Inference: Calibrating Prediction Intervals For Time Seriescited by 12
  29. 2024Kernel-based Optimally Weighted Conformal Time-Series Predictioncited by 12
  30. 2024Split Conformal Prediction under Data Contaminationcited by 12
  31. 2024Are foundation models for computer vision good conformal predictors?cited by 12
  32. 2022Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Surveycited by 11
  33. 2023Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learningcited by 11
  34. 2024Early Time Classification with Accumulated Accuracy Gap Controlcited by 11
  35. 2024Conformal online model aggregationcited by 11
  36. 2024The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learningcited by 11
  37. 2024Multi-Source Conformal Inference Under Distribution Shiftcited by 11
  38. 2024Normalizing Flows for Conformal Regressioncited by 11

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