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Curriculum Learning of Bayesian Network Structures
Date: 2016/5/21             Browse: 544

Paper author list:

Yanpeng Zhao, Yetian Chen, Kewei Tu, Jin Tian


Published time



Paper title

Curriculum Learning of Bayesian Network Structures


Paper abstract

Bayesian networks (BNs) are directed graphical models that have been widely used in various tasks for probabilistic reasoning and causal modeling. One major challenge in these tasks is to learn the BN structures from data. In this paper, we propose a novel heuristic algorithm for BN structure learning that takes advantage of the idea of \emph{curriculum learning}. Our algorithm learns the BN structure by stages. At each stage a subnet is learned over a selected subset of the random variables conditioned on fixed values of the rest of the variables. The selected subset grows with stages and eventually includes all the variables. We prove theoretical advantages of our algorithm and also empirically show that it outperformed the state-of-the-art heuristic approach in learning BN structures.


Conference or journals name and website

the 7th Asian Conference on Machine Learning (ACML 2015)


A short description of the conference or the journal:

The Asian Conference on Machine Learning (ACML) is an international conference in the area of machine learning. It aims at providing a leading international forum for researchers in Machine Learning and related fields to share their new ideas and achievements. The 7th Asian Conference on Machine Learning (ACML2015) was held in Hong Kong on November 20-22, 2015.