mlsanityData to 14 Oct 2026

Conformal Prediction under Levy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations

arXiv 2502.14105 ↗First posted 19 Feb 2025stat.ML cs.LG math.STcited by 12

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Abstract (arXiv)

Conformal prediction provides a powerful framework for constructing prediction intervals with finite-sample guarantees, yet its robustness under distribution shifts remains a significant challenge. This paper addresses this limitation by modeling distribution shifts using Levy-Prokhorov (LP) ambiguity sets, which capture both local and global perturbations. We provide a self-contained overview of LP ambiguity sets and their connections to popular metrics such as Wasserstein and Total Variation. We show that the link between conformal prediction and LP ambiguity sets is a natural one: by propagating the LP ambiguity set through the scoring function, we reduce complex high-dimensional distribution shifts to manageable one-dimensional distribution shifts, enabling exact quantification of worst-case quantiles and coverage. Building on this analysis, we construct robust conformal prediction intervals that remain valid under distribution shifts, explicitly linking LP parameters to interval width and confidence levels. Experimental results on real-world datasets demonstrate the effectiveness of the proposed approach.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 36 of its 50 references that are in the map. First posted on arXiv 19 Feb 2025.

Builds on

36 of 50 references.

References · 36

  1. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,375
  2. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,340
  3. 2016Universal adversarial perturbationscited by 784
  4. 2019Conformal Prediction Under Covariate Shiftcited by 519
  5. 2021Adaptive Conformal Inference Under Distribution Shiftcited by 375
  6. 2022Conformal prediction beyond exchangeabilitycited by 355
  7. 2022Adaptive Conformal Predictions for Time Seriescited by 161
  8. 2020Conformal Inference of Counterfactuals and Individual Treatment Effectscited by 151
  9. 2022Conformal Inference for Online Prediction with Arbitrary Distribution Shiftscited by 128
  10. 2022Safe Planning in Dynamic Environments using Conformal Predictioncited by 119
  11. 2021Distribution-free uncertainty quantification for classification under label shiftcited by 101
  12. 2024Theoretical Foundations of Conformal Predictioncited by 101
24 more
  1. 2020The iWildCam 2020 Competition Datasetcited by 86
  2. 2020Robust Validation: Confident Predictions Even When Distributions Shiftcited by 82
  3. 2022Sequential Predictive Conformal Inference for Time Seriescited by 75
  4. 2021Fair Conformal Predictors for Applications in Medical Imagingcited by 74
  5. 2018Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Datacited by 66
  6. 2022Practical Adversarial Multivalid Conformal Predictioncited by 65
  7. 2021Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approachcited by 62
  8. 2022Conformal Prediction Under Feedback Covariate Shift for Biomolecular Designcited by 54
  9. 2021Conformalized Survival Analysiscited by 53
  10. 2021Conformal Sensitivity Analysis for Individual Treatment Effectscited by 40
  11. 2022Doubly Robust Calibration of Prediction Sets under Covariate Shiftcited by 40
  12. 2020Certifying Confidence via Randomized Smoothingcited by 36
  13. 2022Conformal Prediction for STL Runtime Verificationcited by 35
  14. 2024Provably Robust Conformal Prediction with Improved Efficiencycited by 27
  15. 2023Probabilistically robust conformal predictioncited by 25
  16. 2022Prediction Sets Adaptive to Unknown Covariate Shiftcited by 22
  17. 2020Valid model-free spatial predictioncited by 19
  18. 2022Holistic Robust Data-Driven Decisionscited by 18
  19. 2022Conformalized survival analysis with adaptive cutoffscited by 17
  20. 2024Not all distributional shifts are equal: Fine-grained robust conformal inferencecited by 16
  21. 2024Robust Yet Efficient Conformal Prediction Setscited by 16
  22. 2025Wasserstein-regularized Conformal Prediction under General Distribution Shiftcited by 15
  23. 2024Split Conformal Prediction under Data Contaminationcited by 12
  24. 2022Distributional Uncertainty Propagation via Optimal Transportcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 10.

12 papers.

  1. 2025From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centeredcited by 21
  2. 2025Efficient Robust Conformal Prediction via Lipschitz-Bounded Networkscited by 4
  3. 2026Conformal Prediction for Generative Models via Adaptive Cluster-Based Density Estimationcited by 2
  4. 2026When Environments Shift: Safe Planning with Generative Priors and Robust Conformal Predictioncited by 2
  5. 2026Conformal Prediction via Transported Beta Lawscited by 2
  6. 2026Coverage Guarantees for Pseudo-Calibrated Conformal Prediction under Distribution Shiftcited by 1
  7. 2026When Does Trimming Help Conformal Prediction? A Retained-Law Diagnostic under Calibration Contaminationcited by 1
  8. 2026Conformalized Large Language Models under Configuration Shiftcited by 1
  9. 2026A Unified Perspective on Conformal Prediction and Wasserstein Distributionally Robust Optimization for Uncertainty Quantificationcited by 0
  10. 2026Multi-source conformal prediction: leveraging heterogeneity via localizationcited by 0
  11. 2026Conformal Bayes under Continuous Label Shift: Sensitivity Analysis and the Limits of Exact Validitycited by 0
  12. 2026Multivariate conformal uncertainty propagation in multitask atomistic simulation: Successes and pitfallscited by 0