# Conformal calibrators

arXiv: https://arxiv.org/abs/1902.06579 · HTML: https://mlsanity.com/paper/1902.06579/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2019.

## Built on by (most cited first, 22 of 22)

- [Distribution-Free, Risk-Controlling Prediction Sets](https://mlsanity.com/paper/2101.02703/) (2021)
- [Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control](https://mlsanity.com/paper/2110.01052/) (2021)
- [Calibration in Deep Learning: A Survey of the State-of-the-Art](https://mlsanity.com/paper/2308.01222/) (2023)
- [A Large-Scale Study of Probabilistic Calibration in Neural Network Regression](https://mlsanity.com/paper/2306.02738/) (2023)
- [T-Cal: An optimal test for the calibration of predictive models](https://mlsanity.com/paper/2203.01850/) (2022)
- [Calibration by Distribution Matching: Trainable Kernel Calibration Metrics](https://mlsanity.com/paper/2310.20211/) (2023)
- [Private Prediction Sets](https://mlsanity.com/paper/2102.06202/) (2021)
- [Modular Conformal Calibration](https://mlsanity.com/paper/2206.11468/) (2022)
- [Recommendation Systems with Distribution-Free Reliability Guarantees](https://mlsanity.com/paper/2207.01609/) (2022)
- [Normalizing Flows for Conformal Regression](https://mlsanity.com/paper/2406.03346/) (2024)
- [Online Calibrated and Conformal Prediction Improves Bayesian Optimization](https://mlsanity.com/paper/2112.04620/) (2021)
- [How Safe Am I Given What I See? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy](https://mlsanity.com/paper/2308.12252/) (2023)
- [DIFR3CT: Latent Diffusion for Probabilistic 3D CT Reconstruction from Few Planar X-Rays](https://mlsanity.com/paper/2408.15118/) (2024)
- [Is this model reliable for everyone? Testing for strong calibration](https://mlsanity.com/paper/2307.15247/) (2023)
- [Sharp Calibrated Gaussian Processes](https://mlsanity.com/paper/2302.11961/) (2023)
- [Conformal Predictive Systems Under Covariate Shift](https://mlsanity.com/paper/2404.15018/) (2024)
- [Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors](https://mlsanity.com/paper/2207.08200/) (2022)
- [Multivariate Latent Recalibration for Conditional Normalizing Flows](https://mlsanity.com/paper/2505.16636/) (2025)
- [Adaptive Conformal Guidance for Learning under Uncertainty](https://mlsanity.com/paper/2502.16736/) (2025)
- [How Safe Will I Be Given What I Saw? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy](https://mlsanity.com/paper/2508.09346/) (2025)
- [Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimation](https://mlsanity.com/paper/2312.07952/) (2023)
- [Prediction Intervals for Individual Treatment Effects in a Multiple Decision Point Framework using Conformal Inference](https://mlsanity.com/paper/2512.08828/) (2025)

## 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.