Leveraging Domain Knowledge in Multitask Bayesian Network Structure Learning

By Diane Oyen, Terran Lane. 2012

32 citations recorded by the source corpus.

Network structure learning algorithms have aided network discovery in fields such as bioinformatics, neuroscience, ecology and social science. However, challenges remain in learning informative networks for related sets of tasks because the search space of Bayesian network structures is characterized by large basins of approximately equivalent solutions. Multitask algorithms select a set of networks that are near each other in the search space, rather than a score-equivalent set of networks chosen from independent regions of the space. This selection preference allows a domain expert to see only differences supported by the data. However, the usefulness of these algorithms for scientific datasets is limited because existing algorithms naively assume that all pairs of tasks are equally related. We introduce a framework that relaxes this assumption by incorporating domain knowledge about task-relatedness into the learning objective. Using our framework, we introduce the first multitask Bayesian network algorithm that leverages domain knowledge about the relatedness of tasks. We use our algorithm to explore the effect of task-relatedness on network discovery and show that our algorithm learns networks that are closer to ground truth than naive algorithms and that our algorithm discovers patterns that are interesting.

Subfields: Artificial Intelligence, Management Science and Operations Research, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mohammad Rostami
2. Qiang Yang
3. Yu Zhang
4. Eric Eaton
5. Jie Lu
6. David Isele
7. Guangquan Zhang
8. Mingsheng Long

In Management Science and Operations Research

1. Qiang Yang
2. Eric Eaton
3. Mohammad Rostami
4. Jie Lu
5. David Isele
6. Guangquan Zhang
7. Yu Zhang
8. Rahaf Aljundi

In Molecular Biology

1. Yu Zhang
2. Eric Eaton
3. Mohammad Rostami
4. Qiang Yang

Citations in the corpus

• A Survey on Multi-Task Learning (2017, background): “In [149], some domain knowledge about task relations is incorporated into the learning of multiple Bayesian networks in different tasks.”
• Transfer learning using computational intelligence: A survey (2015, background): “As a result, Oyen and Lane [78] relaxed this assumption by adding a task relatedness metric, which explicitly controls the amount of information sharing between tasks, into a learning objective to incorporate domain knowledge about task-relatedness.”
• Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer (2017, background): “For example, in multi-task medical domains, patients are often grouped into tasks by demographic data and disease presentation (Oyen & Lane, 2012).”
• Learning Transferable Knowledge Through Embedding Spaces (2019, background): “patients are often grouped into tasks by demographic data and disease presentation [191].”