Finding Structure and Causality in Linear Programs
Abstract
Linear Programs (LP) are celebrated widely, particularly so in machine learning where they have allowed for effectively solving probabilistic inference tasks or imposing structure on end-to-end learning systems. Their potential might seem depleted but we propose a foundational, causal perspective that reveals intriguing intra- and inter-structure relations for LP components. We conduct a systematic, empirical investigation on general-, shortest path- and energy system LPs.
Cite
Text
Zečević et al. "Finding Structure and Causality in Linear Programs." ICLR 2022 Workshops: OSC, 2022.Markdown
[Zečević et al. "Finding Structure and Causality in Linear Programs." ICLR 2022 Workshops: OSC, 2022.](https://mlanthology.org/iclrw/2022/zecevic2022iclrw-finding/)BibTeX
@inproceedings{zecevic2022iclrw-finding,
title = {{Finding Structure and Causality in Linear Programs}},
author = {Zečević, Matej and Busch, Florian Peter and Dhami, Devendra Singh and Kersting, Kristian},
booktitle = {ICLR 2022 Workshops: OSC},
year = {2022},
url = {https://mlanthology.org/iclrw/2022/zecevic2022iclrw-finding/}
}