mlsanityData to 7 Oct 2026

Conformal calibrators

arXiv 1902.06579 ↗First posted 18 Feb 2019cs.LG stat.MLcited by 22

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Abstract (arXiv)

Most existing examples of full conformal predictive systems, split-conformal predictive systems, and cross-conformal predictive systems impose severe restrictions on the adaptation of predictive distributions to the test object at hand. In this paper we develop split-conformal and cross-conformal predictive systems that are fully adaptive. Our method consists in calibrating existing predictive systems; the input predictive system is not supposed to satisfy any properties of validity, whereas the output predictive system is guaranteed to be calibrated in probability. It is interesting that the method may also work without the IID assumption, standard in conformal prediction.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Feb 2019.

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Citing papers per year: 2021: 4; 2022: 4; 2023: 7; 2024: 3; 2025: 4.

22 papers.

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  1. 2021Distribution-Free, Risk-Controlling Prediction Setscited by 278
  2. 2021Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Controlcited by 256
  3. 2023Calibration in Deep Learning: A Survey of the State-of-the-Artcited by 72
  4. 2023A Large-Scale Study of Probabilistic Calibration in Neural Network Regressioncited by 26
  5. 2022T-Cal: An optimal test for the calibration of predictive modelscited by 24
  6. 2023Calibration by Distribution Matching: Trainable Kernel Calibration Metricscited by 20
  7. 2021Private Prediction Setscited by 16
  8. 2022Modular Conformal Calibrationcited by 15
  9. 2022Recommendation Systems with Distribution-Free Reliability Guaranteescited by 13
  10. 2024Normalizing Flows for Conformal Regressioncited by 11
  11. 2021Online Calibrated and Conformal Prediction Improves Bayesian Optimizationcited by 10
  12. 2023How Safe Am I Given What I See? Calibrated Prediction of Safety Chances for Image-Controlled Autonomycited by 8
10 more
  1. 2024DIFR3CT: Latent Diffusion for Probabilistic 3D CT Reconstruction from Few Planar X-Rayscited by 8
  2. 2023Is this model reliable for everyone? Testing for strong calibrationcited by 6
  3. 2023Sharp Calibrated Gaussian Processescited by 5
  4. 2024Conformal Predictive Systems Under Covariate Shiftcited by 5
  5. 2022Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priorscited by 3
  6. 2025Multivariate Latent Recalibration for Conditional Normalizing Flowscited by 3
  7. 2025Adaptive Conformal Guidance for Learning under Uncertaintycited by 2
  8. 2025How Safe Will I Be Given What I Saw? Calibrated Prediction of Safety Chances for Image-Controlled Autonomycited by 1
  9. 2023Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimationcited by 0
  10. 2025Prediction Intervals for Individual Treatment Effects in a Multiple Decision Point Framework using Conformal Inferencecited by 0