DPPy_paper

This is the companion paper associated with the DPPy Python library, itself supported by an extensive documentation.

We wrote this companion paper to DPPy, for latter submission to the MLOSS track of JMLR.

The companion paper is available on:

• arXiv (maybe not upto date)
• here on GitHub for the lastest version

Build the PDF

If you are on this page, you most likely already have git:

git clone https://github.com/guilgautier/DPPy_paper.git
cd DPPy_paper/tex

Then, you need a full LaTeX installation, like TeXlive or MikTex to build the pdf:

pdflatex dppy_paper.tex

If you use this package, please consider citing it with this piece of BibTeX:

How to cite this work?

If you use the DPPy package, please consider citing it with this piece of BibTeX:

@article{GaBaVa18,
    archivePrefix = {arXiv},
    arxivId = {1809.07258},
    author = {Gautier, Guillaume and Bardenet, R{\'{e}}mi and Valko, Michal},
    eprint = {1809.07258},
    journal = {ArXiv e-prints},
    title = {{DPPy: Sampling Determinantal Point Processes with Python}},
    keywords = {Computer Science - Machine Learning, Computer Science - Mathematical Software, Statistics - Machine Learning},
    url = {http://arxiv.org/abs/1809.07258},
    year = {2018},
    note = {Code at http://github.com/guilgautier/DPPy/ Documentation at http://dppy.readthedocs.io/}
}

Reproducibility

We would like to thank Guillermo Polito for leading our reproducible research workgroup, this project owes him a lot.

Take a look at the corresponding booklet to learn more on how to make your research reproducible!