Learning Sparse Nonparametric DAGs
Learning Sparse Nonparametric DAGs
复制标题
DOI：
--
复制
发表时间：
2019-09
期刊：
影响因子：
--
通讯作者：
Xun Zheng;Chen Dan;Bryon Aragam;Pradeep Ravikumar;E. Xing
中科院分区：
文献类型：
--
作者：
Xun Zheng;Chen Dan;Bryon Aragam;Pradeep Ravikumar;E. Xing
我们开发了一个从数据中学习稀疏非参数有向无环图（DAG）的框架。我们的方法是基于最近的代数特征的DAG，导致了一个完全连续的程序，基于分数的学习的DAG模型参数化的线性结构方程模型（SEM）。我们利用非参数稀疏偏导数的基础上，扩展这种代数特征的非参数SEM，从而在一个连续的优化问题，可以应用于各种非参数和半参数模型，包括GLM，加性噪声模型，指数模型作为特殊情况。与需要特定建模选择，损失函数或算法的现有方法不同，我们提出了一个完全通用的框架，可以应用于一般非线性模型（例如，没有加性噪声），一般可微损失函数和通用黑盒优化例程。代码可以在这个https URL上找到。
We develop a framework for learning sparse nonparametric directed acyclic graphs (DAGs) from data. Our approach is based on a recent algebraic characterization of DAGs that led to a fully continuous program for score-based learning of DAG models parametrized by a linear structural equation model (SEM). We extend this algebraic characterization to nonparametric SEM by leveraging nonparametric sparsity based on partial derivatives, resulting in a continuous optimization problem that can be applied to a variety of nonparametric and semiparametric models including GLMs, additive noise models, and index models as special cases. Unlike existing approaches that require specific modeling choices, loss functions, or algorithms, we present a completely general framework that can be applied to general nonlinear models (e.g. without additive noise), general differentiable loss functions, and generic black-box optimization routines. The code is available at this https URL.