SPQR is a python toolbox for optimization of superquantile-based risk measures. This toolbox comes with the companion paper "First-order optimization for superquantile-based supervised learning", published in the proceedings of MLSP 2020.

For a more detailed description of the toolbox and the setup instructions see the documentation.

Yassine Laguel
Jérôme Malick
Zaid Harchaoui

This project is licensed under the GPLv3 License - see the LICENSE.md file for details.

If you found this package useful, please cite the following work.

@inproceedings{laguel-etal:spqr:mlsp2020,
  title={First-order optimization for superquantile-based supervised learning},
  author={Laguel, Yassine and Malick, Jérôme and Harchaoui, Zaid},
  booktitle={2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)},
  pages={1--6},
  year={2020},
  organization={IEEE}
}