Distribution-Independent Confidence intervals for the Eigendecomposition of Covariance Matrices

This is the code that accompanies the paper Distribution-Independent Confidence Intervals for the Eigendecomposition of Covariance Matrices via the Eigenvalue-Eigenvector Identity, accepted at ICML 2021 Workshop on Distribution-Free Uncertainty Quantification.

Installation

conda env create -f conda_env_<YOUR_OS>.yml
conda activate confidence_intervals
pip install -e .

Experiments

To run the experiments, use the run.py script in cieg/experiments.

Reference

If you found this code useful, please cite:

@incollection{Popordanoska2021b,
year={2021},
booktitle={ICML Workshop on Distribution Free Uncertainty Quantification},
title={Distribution-Independent Confidence Intervals for the Eigendecomposition of Covariance Matrices via the Eigenvalue-Eigenvector Identity},
author={Teodora Popordanoska and Aleksei Tiulpin and Wacha Bounliphone and Blaschko, Matthew B.},
}

License

Everything is licensed under the MIT License.

Acknowledgements

We acknowledge funding from the Flemish Government under the "Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen" programme.