具有统计保证的非参数有向无环图的有效学习

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2023-01-01 DOI:10.5705/ss.202022.0272

Yibo Deng, Xin He, Shaogao Lv

引用次数: 0

摘要

有向无环图(DAG)模型被广泛用于表示所收集节点之间的因果关系。本文提出了一种具有一般因果依赖结构的高效一致的DAG学习方法，这与现有的大多数假设因果关系线性依赖的方法形成了鲜明对比。为了方便DAG学习，该方法利用拓扑层的概念，将非参数DAG学习与光滑再现核希尔伯特空间(RKHS)中的核脊回归和学习梯度联系起来，表明通过基于核的估计可以精确地重建非参数DAG的拓扑层，并且通过计算估计的梯度函数可以直接获得父-子关系。所开发的算法在计算上是高效的，因为它试图用解析解来解决一个凸优化问题，梯度的吕少高是相应的作者;作者对本文的贡献相同，他们的名字按字母顺序排列。中国统计:新录用论文(接受作者版本，需英文编辑)

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.