Curriculum Learning of Bayesian Network Structures

Paper author list:

Yanpeng Zhao, Yetian Chen, Kewei Tu, Jin Tian

Published time：

2015

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 ofthe rest of the variables. The selected subset grows with stages and eventually includes all the variables. We prove theoretical advantages of our algorithmand also empirically show that it outperformed the state-of-the-art heuristic approach in learning BN structures.

Conference or journal’s name and website：

the 7th Asian Conference on Machine Learning (ACML 2015)，http://acml-conf.org/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.