mlsanityData to 14 Oct 2026

Non-exchangeable Conformal Prediction with Optimal Transport: Tackling Distribution Shifts with Unlabeled Data

arXiv 2507.10425 ↗First posted 14 Jul 2025cs.LG stat.MLcited by 5

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Abstract (arXiv)

Conformal prediction is a distribution-free uncertainty quantification method that has gained popularity in the machine learning community due to its finite-sample guarantees and ease of use. Its most common variant, dubbed split conformal prediction, is also computationally efficient as it boils down to collecting statistics of the model predictions on some calibration data not yet seen by the model. Nonetheless, these guarantees only hold if the calibration and test data are exchangeable, a condition that is difficult to verify and often violated in practice due to so-called distribution shifts. The literature is rife with methods to mitigate the loss in coverage in this non-exchangeable setting, but these methods require some prior information on the type of distribution shift to be expected at test time. In this work, we study this problem via a new perspective, through the lens of optimal transport, and show that it is possible to estimate the loss in coverage and mitigate arbitrary distribution shifts, offering a principled and broadly applicable solution.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 31 of its 51 references that are in the map. First posted on arXiv 14 Jul 2025.

Builds on

31 of 51 references.

References · 31

  1. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  2. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,877
  3. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,375
  4. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 792
  5. 2019Conformal Prediction Under Covariate Shiftcited by 519
  6. 2020Classification with Valid and Adaptive Coveragecited by 396
  7. 2021Adaptive Conformal Inference Under Distribution Shiftcited by 375
  8. 2022Conformal prediction beyond exchangeabilitycited by 355
  9. 2022Adaptive Conformal Predictions for Time Seriescited by 161
  10. 2023Conformal Prediction With Conditional Guaranteescited by 152
  11. 2020Conformal Inference of Counterfactuals and Individual Treatment Effectscited by 151
  12. 2022Conformal Inference for Online Prediction with Arbitrary Distribution Shiftscited by 128
19 more
  1. 2021Learning Optimal Conformal Classifierscited by 112
  2. 2020Telescoping Density-Ratio Estimationcited by 108
  3. 2021Distribution-free uncertainty quantification for classification under label shiftcited by 101
  4. 2020The iWildCam 2020 Competition Datasetcited by 86
  5. 2018Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Datacited by 66
  6. 2021Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approachcited by 62
  7. 2020Conformal prediction for time seriescited by 53
  8. 2022Doubly Robust Calibration of Prediction Sets under Covariate Shiftcited by 40
  9. 2024Length Optimization in Conformal Predictioncited by 39
  10. 2023Conformal prediction under ambiguous ground truthcited by 31
  11. 2023RLSbench: Domain Adaptation Under Relaxed Label Shiftcited by 30
  12. 2023Non-Exchangeable Conformal Risk Controlcited by 29
  13. 2021Optimized conformal classification using gradient descent approximationcited by 28
  14. 2022Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecastingcited by 27
  15. 2022Prediction Sets Adaptive to Unknown Covariate Shiftcited by 22
  16. 2024An Information Theoretic Perspective on Conformal Predictioncited by 21
  17. 2024Conformalized Credal Regions for Classification with Ambiguous Ground Truthcited by 19
  18. 2024Conformal Prediction via Regression-as-Classificationcited by 18
  19. 2025Wasserstein-regularized Conformal Prediction under General Distribution Shiftcited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Conformal Policy Controlcited by 6
  2. 2026Conformal Prediction via Transported Beta Lawscited by 2
  3. 2026Coverage Guarantees for Pseudo-Calibrated Conformal Prediction under Distribution Shiftcited by 1
  4. 2026Certify or Refuse: A Cross-Model Map for Selective Risk Control with Coverage Floors under Covariate Shiftcited by 0
  5. 2026CHOIR: heterogeneity-aware conformal prediction for crash injury severity across driver safety stratacited by 0