srlearn: A Python Library for Gradient-Boosted Statistical Relational Models

Alexander L. Hayes
ProHealth Lab
Indiana University Bloomington
[email protected]

Accepted at the Ninth International Workshop on Statistical Relational AI.

Please contact Alexander at [email protected] with any questions. This repository is aimed at hosting supplementary material, and may not be updated significantly in the future.

Overview

This repository contains a copy of the submitted PDF, the LaTeX source to reproduce the PDF, scripts to reproduce the "Experiments" subsection, links, and additional material that did not make it into the workshop paper.

Paper

• 📄 Read Online (PDF - hayesall.com)
  • Preprint - arXiv:1912.08198 [cs.LG]
  • DeepAI Publication Page
• 📥 Download a PDF

Software

Results of the paper are based on srlearn==0.5.0

Please consider starring 🌟 the srlearn GitHub Repository repository. It's an open-source project, so any feedback or recommendations are appreciated.

Experiments

Scripts for reproducing Table 1 are contained in the experiments/ directory.

• experiments/ directory

Examples are licensed under the terms of the MIT License.

Citing

If you build on this code or the ideas of the paper, please consider citing:

@article{hayes2020srlearn,
  author = {Alexander L. Hayes},
  title = {{srlearn: A Python Library for Gradient-Boosted Statistical Relational Models}},
  year = {2020},
  journal = {Ninth International Workshop on Statistical Relational AI}
}

Acknowledgements

ALH is sponsored through Indiana University's "Precision Health Initiative" (PHI) Grand Challenge. ALH would like to thank Sriraam Natarajan, Travis LaGrone, and members of the StARLinG Lab at the University of Texas at Dallas.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.