https://github.com/machinelearningbcam/rmboost-neurips-2025

https://github.com/machinelearningbcam/rmboost-neurips-2025

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• Host: GitHub
• URL: https://github.com/machinelearningbcam/rmboost-neurips-2025
• Owner: MachineLearningBCAM
• License: mit
• Created: 2025-09-26T17:49:16.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-10-27T21:56:12.000Z (9 months ago)
• Last Synced: 2025-10-27T23:23:13.115Z (9 months ago)
• Language: Python
• Size: 20.5 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Robust Minimax Boosting with Performance Guarantees (RMBoost)

[![made-with-python](https://img.shields.io/badge/Made%20with-Python-1f425f.svg)](/AMRC_Python) [![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)

This repository is the official implementation of Robust Minimax Boosting with Performance Guarantees

RMBoost methods are robust to general types of label noise and can also achieve strong classification performance.

## Source code

[![made-with-python](https://img.shields.io/badge/Made%20with-Python-1f425f.svg)](CL-MRC_Python) 

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

mMBoost folder contains the Python and Matlab folders that include the Python and Matlab implementations, respectively.

### Python code

* run_RMBoost.py is the main file. In such file we can modify the number of rounds and the solver (linprog or mosek)

* RMBoost.py is the file that includes fit and predict functions

#### Requirements

The requirements are detailed in the requeriments.txt file. Run the following command to install the requeriments:

```setup

pip install -r requirements.txt

```

### Matlab code

* main.m is the main file. In such file we can modify the number of rounds and the solver (linprog or mosek)

* fit.m is the function that fits the model

* predict_boost.m is the function that obtains the predictions

## Installation and evaluation

To train and evaluate the model in the paper, run this command for Python:

```console

python run_RMboost.py

```

and for Matlab:

```console

matlab RMBoost.m

```

## Support and Author

Santiago Mazuelas

smazuelas@bcamath.org

Verónica Álvarez

vealvar@mit.edu

[![ForTheBadge built-with-science](http://ForTheBadge.com/images/badges/built-with-science.svg)](https://github.com/VeronicaAlvarez)

## License 

RMBoost 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{MazAlv:25,

  title      ={Robust Minimax Boosting with Performance Guarantees},

  author     ={Mazuelas, Santiago and {\'A}lvarez, Ver{\'o}nica},

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

  volume     ={38},

  pages      ={},

  year       ={2025},

  month      ={Dec.}

}