Active Learning of Transfer Relationships for Multiple Related Bayesian Network Structures

Multitask network structure learning is an important problem in several scientific domains, such as, computational neuroscience and bioinformatics. However, existing algorithms do not leverage valuable domain knowledge about the relatedness of tasks. We present the first multitask Bayesian network learning algorithm that incorporates task-relatedness. Empirical results demonstrate that our algorithm learns more robust networks than existing algorithms. Defining the tasks themselves is also a challenge for multitask learning. Typically, the data is a priori partitioned into tasks. However, domain experts often modify the splitting of data into tasks based on the learned networks and then re-run the multitask algorithm with a new data partitioning. We introduce a framework to actively learn the tasks as data partitions using feedback from a domain expert.