论文标题
Strudel：学习结构化解释的概率电路
Strudel: Learning Structured-Decomposable Probabilistic Circuits
论文作者
论文摘要
概率电路（PC）表示概率分布作为计算图。在这些图上执行结构属性可确保几种推理情景变得可行。在这些属性中，结构化的可分解性是一个特别有吸引力的：它可以对复杂逻辑公式的概率进行有效而精确的计算，并可用于对丢失数据下某些预测模型的预期输出进行推理。本文提出了Strudel，这是一种简单，快速，准确的学习算法，用于结构化解释的PC。与学习结构化可解释的PC的先前工作相比，Strudel以更少的迭代方式提供了更准确的单个PC模型，并且在构建PC的集合时会大幅度地扩展学习。它通过利用PC的另一个结构性（称为确定论）以及在混合组件之间共享相同的计算图，从而实现了这种可伸缩性。我们在标准密度估计基准和具有挑战性的推理方案上显示了这些优势。
Probabilistic circuits (PCs) represent a probability distribution as a computational graph. Enforcing structural properties on these graphs guarantees that several inference scenarios become tractable. Among these properties, structured decomposability is a particularly appealing one: it enables the efficient and exact computations of the probability of complex logical formulas, and can be used to reason about the expected output of certain predictive models under missing data. This paper proposes Strudel, a simple, fast and accurate learning algorithm for structured-decomposable PCs. Compared to prior work for learning structured-decomposable PCs, Strudel delivers more accurate single PC models in fewer iterations, and dramatically scales learning when building ensembles of PCs. It achieves this scalability by exploiting another structural property of PCs, called determinism, and by sharing the same computational graph across mixture components. We show these advantages on standard density estimation benchmarks and challenging inference scenarios.
