Bayesian Discovery of Multiple Bayesian Networks via Transfer Learning

TL;DR

This paper introduces a transfer learning approach for Bayesian structure discovery that improves the identification of true network edges across related tasks, especially with limited data, and demonstrates its effectiveness on neuroimaging data.

Contribution

It presents a novel transfer learning framework for Bayesian structure discovery that allows exploration of shared and unique features among related tasks, with efficient local computations.

Findings

Transfer learning improves true edge identification over single-task methods.

The approach is computationally efficient due to local factorization.

Empirical results on neuroimaging data validate the method's effectiveness.

Abstract

Bayesian network structure learning algorithms with limited data are being used in domains such as systems biology and neuroscience to gain insight into the underlying processes that produce observed data. Learning reliable networks from limited data is difficult, therefore transfer learning can improve the robustness of learned networks by leveraging data from related tasks. Existing transfer learning algorithms for Bayesian network structure learning give a single maximum a posteriori estimate of network models. Yet, many other models may be equally likely, and so a more informative result is provided by Bayesian structure discovery. Bayesian structure discovery algorithms estimate posterior probabilities of structural features, such as edges. We present transfer learning for Bayesian structure discovery which allows us to explore the shared and unique structural features among related tasks. Efficient computation requires that our transfer learning objective factors into local calculations, which we prove is given by a broad class of transfer biases. Theoretically, we show the efficiency of our approach. Empirically, we show that compared to single task learning, transfer learning is better able to positively identify true edges. We apply the method to whole-brain neuroimaging data.