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Elements of Conformal Prediction

arXiv 2603.23923 ↗First posted 25 Mar 2026stat.ME stat.MLcited by 0

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Abstract (arXiv)

Predictive inference is a fundamental task in statistics, traditionally addressed using parametric assumptions about the data distribution and detailed analyses of how models learn from data. In recent years, conformal prediction has emerged as an alternative framework that is well suited to modern applications involving high-dimensional data and complex machine learning models. Its appeal stems from being both distribution-free---relying mainly on symmetry assumptions such as exchangeability---and model-agnostic, treating the learning algorithm as a black box. Even under such limited assumptions, conformal prediction provides exact finite-sample guarantees, although these are typically marginal and require careful interpretation. This paper explains the core ideas of conformal prediction and reviews selected methods. Rather than offering an exhaustive survey, it aims to provide a clear conceptual entry point and a pedagogical overview of the field.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 110 of its 152 references that are in the map. First posted on arXiv 25 Mar 2026.

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References · 110

  1. 2016Distribution-Free Predictive Inference For Regressioncited by 657
  2. 2019Conformalized Quantile Regressioncited by 620
  3. 2019Conformal Prediction Under Covariate Shiftcited by 511
  4. 2020Classification with Valid and Adaptive Coveragecited by 390
  5. 2020Uncertainty Sets for Image Classifiers using Conformal Predictioncited by 365
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  12. 2023Conformal Prediction With Conditional Guaranteescited by 146
38 more
  1. 2021Testing for Outliers with Conformal p-valuescited by 133
  2. 2023Conformal Prediction with Large Language Models for Multi-Choice Question Answeringcited by 131
  3. 2024Language Models with Conformal Factuality Guaranteescited by 128
  4. 2022Conformal Inference for Online Prediction with Arbitrary Distribution Shiftscited by 123
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  7. 2021Localized Conformal Prediction: A Generalized Inference Framework for Conformal Predictioncited by 112
  8. 2022Safe Planning in Dynamic Environments using Conformal Predictioncited by 112
  9. 2021Learning Optimal Conformal Classifierscited by 108
  10. 2019Distributional conformal predictioncited by 104
  11. 2021Distribution-free uncertainty quantification for classification under label shiftcited by 99
  12. 2024Theoretical Foundations of Conformal Predictioncited by 96
  13. 2024Large language model validity via enhanced conformal prediction methodscited by 91
  14. 2023Conformal PID Control for Time Series Predictioncited by 85
  15. 2021Conformal Prediction using Conditional Histogramscited by 77
  16. 2019A comparison of some conformal quantile regression methodscited by 76
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  19. 2024Hypothesis testing with e-valuescited by 70
  20. 2022Selection by Prediction with Conformal p-valuescited by 66
  21. 2022Practical Adversarial Multivalid Conformal Predictioncited by 65
  22. 2019Flexible distribution-free conditional predictive bands using density estimatorscited by 64
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  27. 2021Copula-based conformal prediction for Multi-Target Regressioncited by 53
  28. 2021Conformalized Survival Analysiscited by 53
  29. 2021Selection and Aggregation of Conformal Prediction Setscited by 46
  30. 2023Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictionscited by 46
  31. 2021PAC Prediction Sets Under Covariate Shiftcited by 42
  32. 2020Exchangeability, Conformal Prediction, and Rank Testscited by 40
  33. 2021Conformal Sensitivity Analysis for Individual Treatment Effectscited by 40
  34. 2022Doubly Robust Calibration of Prediction Sets under Covariate Shiftcited by 40
  35. 2025Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agentscited by 37
  36. 2024Conformal Prediction Sets Improve Human Decision Makingcited by 35
  37. 2023Conformal prediction under ambiguous ground truthcited by 31
  38. 2021Optimized conformal classification using gradient descent approximationcited by 27

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