论文标题
在线性程序中查找结构和因果关系
Finding Structure and Causality in Linear Programs
论文作者
论文摘要
线性程序（LP）得到广泛庆祝，尤其是在机器学习中，他们允许有效地解决概率推理任务或对端到端学习系统强加结构。它们的潜力似乎耗尽了，但我们提出了一种基础，因果观点，揭示了LP组件的有趣内部和结构之间的结构关系。我们对通用，最短路径和能源系统LPS进行系统的实证研究。
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.
