论文标题
关于学习环境特定独立性的新观点
A New Perspective on Learning Context-Specific Independence
论文作者
论文摘要
诸如上下文特定独立性（CSI）之类的本地结构在概率图形模型（PGM）文献中引起了广泛关注，因为它促进了大型复杂系统的建模以及与它们的推理。在本文中，我们提供了有关如何从数据中学习CSI的新观点。我们建议首先学习条件概率表（CPT）（例如神经网络）的功能和参数化表示。接下来，我们将此连续函数量化为算术电路表示，以促进有效的推断。第一步，我们可以利用机器学习文献中开发的许多强大工具。在第二步中，我们从可解释的AI中利用了最近开发的分析工具，以学习CSIS。最后，我们从经验和概念上将我们的方法与更传统的可变分解方法进行对比，从而更明确地寻找CSIS。
Local structure such as context-specific independence (CSI) has received much attention in the probabilistic graphical model (PGM) literature, as it facilitates the modeling of large complex systems, as well as for reasoning with them. In this paper, we provide a new perspective on how to learn CSIs from data. We propose to first learn a functional and parameterized representation of a conditional probability table (CPT), such as a neural network. Next, we quantize this continuous function, into an arithmetic circuit representation that facilitates efficient inference. In the first step, we can leverage the many powerful tools that have been developed in the machine learning literature. In the second step, we exploit more recently-developed analytic tools from explainable AI, for the purposes of learning CSIs. Finally, we contrast our approach, empirically and conceptually, with more traditional variable-splitting approaches, that search for CSIs more explicitly.
