Distributed Learning of Generalized Linear Causal Networks
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广义线性因果网络的分布式学习
- 研究问题
- 当数据分散存储于多台机器时，如何学习由广义线性模型参数化的有向无环因果图，并评估分布式计算的表现？
- 方法与思路
- DARLS通过退火搜索拓扑排序，在每个排序下利用本地与中心机器的多轮通信，分布式优化正则化似然评分。
- 材料陈述的贡献
- 摘要称，分布式优化可收敛至全体数据评分的全局最优解，并报告模拟及蛋白质与DNA结合网络应用的表现。
- 阅读关注点
- 应区分分布式优化与整体退火搜索各自的收敛保证，并核对因果图识别条件、通信成本及模拟比较设置。
- 阅读边界
- 这是arXiv预印本，同行评审及接收未核验。摘要未说明因果识别和收敛的具体条件，也未提供性能比较的数值结果。
原文摘要
We consider the task of learning causal structures from data stored on multiple machines, and propose a novel structure learning method called distributed annealing on regularized likelihood score (DARLS) to solve this problem. We model causal structures by a directed acyclic graph that is parameterized with generalized linear models, so that our method is applicable to various types of data. To obtain a high-scoring causal graph, DARLS simulates an annealing process to search over the space of topological sorts, where the optimal graphical structure compatible with a sort is found by a distributed optimization method. This distributed optimization relies on multiple rounds of communication between local and central machines to estimate the optimal structure. We establish its convergence to a global optimizer of the overall score that is computed on all data across local machines. To the best of our knowledge, DARLS is the first distributed method for learning causal graphs with such theoretical guarantees. Through extensive simulation studies, DARLS has shown competing performance against existing methods on distributed data, and achieved comparable structure learning accuracy and test-data likelihood with competing methods applied to pooled data across all local machines. In a real-world application for modeling protein-DNA binding networks with distributed ChIP-Sequencing data, DARLS also exhibits higher predictive power than other methods, demonstrating a great advantage in estimating causal networks from distributed data.