mlsanityData to 7 Oct 2026

Improved conformalized quantile regression

arXiv 2207.02808 ↗First posted 6 Jul 2022stat.ML cs.LGcited by 5

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Abstract (arXiv)

Conformalized quantile regression is a procedure that inherits the advantages of conformal prediction and quantile regression. That is, we use quantile regression to estimate the true conditional quantile and then apply a conformal step on a calibration set to ensure marginal coverage. In this way, we get adaptive prediction intervals that account for heteroscedasticity. However, the aforementioned conformal step lacks adaptiveness as described in (Romano et al., 2019). To overcome this limitation, instead of applying a single conformal step after estimating conditional quantiles with quantile regression, we propose to cluster the explanatory variables weighted by their permutation importance with an optimized k-means and apply k conformal steps. To show that this improved version outperforms the classic version of conformalized quantile regression and is more adaptive to heteroscedasticity, we extensively compare the prediction intervals of both in open datasets.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 16 of its 65 references that are in the map. First posted on arXiv 6 Jul 2022.

Builds on

16 of 65 references.

References · 16

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  3. 2016Variational Inference: A Review for Statisticianscited by 1,466
  4. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 903
  5. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 777
  6. 2016Distribution-Free Predictive Inference For Regressioncited by 657
  7. 2019Conformalized Quantile Regressioncited by 620
  8. 2020Bayesian Neural Networks: An Introduction and Surveycited by 130
  9. 2020Conformal Prediction: a Unified Review of Theory and New Challengescited by 118
  10. 2019Distributional conformal predictioncited by 104
  11. 2021Conformal Prediction using Conditional Histogramscited by 77
  12. 2019A comparison of some conformal quantile regression methodscited by 76
4 more
  1. 2019Adaptive, Distribution-Free Prediction Intervals for Deep Networkscited by 56
  2. 2020Exchangeability, Conformal Prediction, and Rank Testscited by 40
  3. 2021Valid prediction intervals for regression problemscited by 28
  4. 2019Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Marketscited by 19

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 3.

5 papers.

  1. 2024Conformal Prediction: A Data Perspectivecited by 25
  2. 2022A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecastingcited by 1
  3. 2025Interference-Aware Edge Runtime Prediction with Conformal Matrix Completioncited by 1
  4. 2025Decoding Federated Learning: The FedNAM+ Conformal Revolutioncited by 1
  5. 2025Conformal coronary calcification volume estimation with conditional coverage via histogram clusteringcited by 0