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D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
Muhan Zhang · Shali Jiang · Zhicheng Cui · Roman Garnett · Yixin Chen

Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #1

Graph structured data are abundant in the real world. Among different graph types, directed acyclic graphs (DAGs) are of particular interest to machine learning researchers, as many machine learning models are realized as computations on DAGs, including neural networks and Bayesian networks. In this paper, we study deep generative models for DAGs, and propose a novel DAG variational autoencoder (D-VAE). To encode DAGs into the latent space, we leverage graph neural networks. We propose an asynchronous message passing scheme that allows encoding the computations on DAGs, rather than using existing simultaneous message passing schemes to encode local graph structures. We demonstrate the effectiveness of our proposed DVAE through two tasks: neural architecture search and Bayesian network structure learning. Experiments show that our model not only generates novel and valid DAGs, but also produces a smooth latent space that facilitates searching for DAGs with better performance through Bayesian optimization.

Author Information

Muhan Zhang (Washington University; Facebook (now))
Shali Jiang (Washington University in St. Louis)
Zhicheng Cui (Washington University in St. Louis)
Roman Garnett (Washington University in St. Louis)
Yixin Chen (Washington University in St. Louis)

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