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Adjusting Regression Models for Conditional Uncertainty Calibration

arXiv 2409.17466 ↗First posted 26 Sep 2024stat.ML cs.AI cs.LGcited by 0

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Abstract (arXiv)

Conformal Prediction methods have finite-sample distribution-free marginal coverage guarantees. However, they generally do not offer conditional coverage guarantees, which can be important for high-stakes decisions. In this paper, we propose a novel algorithm to train a regression function to improve the conditional coverage after applying the split conformal prediction procedure. We establish an upper bound for the miscoverage gap between the conditional coverage and the nominal coverage rate and propose an end-to-end algorithm to control this upper bound. We demonstrate the efficacy of our method empirically on synthetic and real-world datasets.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 33 references that are in the map. First posted on arXiv 26 Sep 2024.

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17 of 33 references.

References · 17

  1. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 777
  2. 2016Distribution-Free Predictive Inference For Regressioncited by 657
  3. 2019Conformalized Quantile Regressioncited by 620
  4. 2023Conformal Prediction With Conditional Guaranteescited by 146
  5. 2021Learning Optimal Conformal Classifierscited by 108
  6. 2019Distributional conformal predictioncited by 104
  7. 2018Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobservedcited by 94
  8. 2021Conformal Prediction using Conditional Histogramscited by 77
  9. 2019Flexible distribution-free conditional predictive bands using density estimatorscited by 64
  10. 2022Training Uncertainty-Aware Classifiers with Conformalized Deep Learningcited by 62
  11. 2021Improving Conditional Coverage via Orthogonal Quantile Regressioncited by 48
  12. 2022Probabilistic Conformal Prediction Using Conditional Random Samplescited by 47
5 more
  1. 2019Prediction and outlier detection in classification problemscited by 44
  2. 2021Optimized conformal classification using gradient descent approximationcited by 27
  3. 2020Training conformal predictorscited by 20
  4. 2022Split Localized Conformal Predictioncited by 20
  5. 2021Learning Prediction Intervals for Regression: Generalization and Calibrationcited by 16

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