mlsanityData to 7 Oct 2026

Adaptive Conformal Regression with Jackknife+ Rescaled Scores

arXiv 2305.19901 ↗First posted 31 May 2023cs.LG stat.MLcited by 8

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Abstract (arXiv)

Conformal regression provides prediction intervals with global coverage guarantees, but often fails to capture local error distributions, leading to non-homogeneous coverage. We address this with a new adaptive method based on rescaling conformal scores with an estimate of local score distribution, inspired by the Jackknife+ method, which enables the use of calibration data in conformal scores without breaking calibration-test exchangeability. Our approach ensures formal global coverage guarantees and is supported by new theoretical results on local coverage, including an a posteriori bound on any calibration score. The strength of our approach lies in achieving local coverage without sacrificing calibration set size, improving the applicability of conformal prediction intervals in various settings. As a result, our method provides prediction intervals that outperform previous methods, particularly in the low-data regime, making it especially relevant for real-world applications such as healthcare and biomedical domains where uncertainty needs to be quantified accurately despite low sample data.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 21 of its 39 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

21 of 39 references.

References · 21

  1. 2016Distribution-Free Predictive Inference For Regressioncited by 657
  2. 2019Conformalized Quantile Regressioncited by 620
  3. 2022Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasetscited by 599
  4. 2019Conformal Prediction Under Covariate Shiftcited by 511
  5. 2019Does Learning Require Memorization? A Short Tale about a Long Tailcited by 452
  6. 2020Classification with Valid and Adaptive Coveragecited by 390
  7. 2022Conformal prediction beyond exchangeabilitycited by 348
  8. 2020ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Predictioncited by 332
  9. 2022Conformal Risk Controlcited by 290
  10. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 258
  11. 2019Predictive inference with the jackknife+cited by 206
  12. 2021Deep Learning Through the Lens of Example Difficultycited by 139
9 more
  1. 2021Localized Conformal Prediction: A Generalized Inference Framework for Conformal Predictioncited by 112
  2. 2013A Conformal Prediction Approach to Explore Functional Datacited by 85
  3. 2021Improving Conditional Coverage via Orthogonal Quantile Regressioncited by 48
  4. 2019Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applicationscited by 43
  5. 2022Conformal Prediction Sets with Limited False Positivescited by 28
  6. 2021Locally Valid and Discriminative Prediction Intervals for Deep Learning Modelscited by 24
  7. 2019Conformal prediction with localizationcited by 22
  8. 2022Split Localized Conformal Predictioncited by 20
  9. 2023Adaptive Conformal Prediction by Reweighting Nonconformity Scorecited by 16

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Cited by year

Citing papers per year: 2024: 7; 2025: 1.

8 papers.

  1. 2024Length Optimization in Conformal Predictioncited by 39
  2. 2024Conformal Prediction with Learned Featurescited by 23
  3. 2024Probabilistic Conformal Prediction with Approximate Conditional Validitycited by 23
  4. 2025Rectifying Conformity Scores for Better Conditional Coveragecited by 17
  5. 2024Normalizing Flows for Conformal Regressioncited by 11
  6. 2024Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filteringcited by 10
  7. 2024Cross-Validation Conformal Risk Controlcited by 8
  8. 2024Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guaranteescited by 7