https://github.com/MachineLearningBCAM/Unsupervised-conformal-prediction-NeurIPS2025

The provided files implement the method proposed in the paper "Split conformal classification with unsupervised calibration"
https://github.com/MachineLearningBCAM/Unsupervised-conformal-prediction-NeurIPS2025

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The provided files implement the method proposed in the paper "Split conformal classification with unsupervised calibration"

• Host: GitHub
• URL: https://github.com/MachineLearningBCAM/Unsupervised-conformal-prediction-NeurIPS2025
• Owner: MachineLearningBCAM
• License: mit
• Created: 2025-10-08T15:31:49.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2025-11-24T11:41:30.000Z (7 months ago)
• Last Synced: 2025-11-28T01:11:05.321Z (7 months ago)
• Language: MATLAB
• Size: 3.37 MB
• Stars: 1
• Watchers: 0
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-conformal-prediction - code

README

          
# Split Conformal 

Classification with Unsupervised Calibration

[![Made with!](https://img.shields.io/badge/Made%20with-MATLAB-red)](/AMRC_Matlab)  [![Ask Me Anything !](https://img.shields.io/badge/Ask%20me-anything-1abc9c.svg)](#support-and-author)

The provided files implement the proposed method for split conformal prediction with unsupervised calibration samples presented in https://arxiv.org/pdf/2510.07185.

## Source code

[![Made with!](https://img.shields.io/badge/Made%20with-MATLAB-red)](CL-MRC_Matlab)

(/code) folder contains the Matlab files required to execute the method:

*    main.m script that runs the methods presented with the same settings as those in the experimental results shown in the paper using the dataset `USPS' that can be found in the folder '/data'. In addition, the function also obtains results with the conventional approach with supervised calibration samples and the naive approach with unsupervised calibration samples

*    find_quant.m function that finds the conformal quantile using the methods presented

*    select_sigma.m function that selects the bandwidth parameter for the Gaussian kernel used

*    find_p.m function that obtains label probabilities by solving a quadratic optimization problem (using cvx and Mosek solver if variable mosek=1 or using Matlab function if mosek=0)

*    weighted_quantile.m function that determines quantiles for values with corresponding probabilities

*    compute_score.m function that computes values for the adaptive score

## Test case

File main.m obtains set-prediction rules and compute the corresponding coverage probabilities and set sizes for one random partition of USPS dataset.

## Support and Author

Santiago Mazuelas

smazuelas@bcamath.org

## License 

This library carries a MIT license.

## Citation

If you find useful the code in your research, please include explicit mention of our work in your publication with the following corresponding entry in your bibliography:

@inproceedings{Maz:25,

  title      ={Split Conformal Classification with Unsupervised Calibration},

  author     ={Mazuelas, Santiago},

  booktitle  ={{A}dvances in {N}eural {I}nformation {P}rocessing {S}ystems},

  volume     ={38},

  pages      ={},

  year       ={2025},

  month      ={Dec.}

}