# Learning Multitask Gaussian Bayesian Networks

arXiv: https://arxiv.org/abs/2205.05343 · HTML: https://mlsanity.com/paper/2205.05343/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 89 references that are in the map.
- First posted on arXiv 11 May 2022.

## Builds on (most cited first, 12 of 12)

- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Distral: Robust Multitask Reinforcement Learning](https://mlsanity.com/paper/1707.04175/) (2017)
- [Learning Bayesian Networks: The Combination of Knowledge and Statistical Data](https://mlsanity.com/paper/1302.6815/) (2013)
- [Learning Gaussian Networks](https://mlsanity.com/paper/1302.6808/) (2013)
- [The Bayesian Structural EM Algorithm](https://mlsanity.com/paper/1301.7373/) (2013)
- [Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm](https://mlsanity.com/paper/1301.6696/) (2013)
- [Semi-supervised Multitask Learning for Sequence Labeling](https://mlsanity.com/paper/1704.07156/) (2017)
- [Learning Large-Scale Bayesian Networks with the sparsebn Package](https://mlsanity.com/paper/1703.04025/) (2017)
- [Transfer Learning in High-dimensional Semi-parametric Graphical Models with Application to Brain Connectivity Analysis](https://mlsanity.com/paper/2112.13356/) (2021)
- [Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care](https://mlsanity.com/paper/1802.05027/) (2018)
- [Multi-task Sparse Structure Learning](https://mlsanity.com/paper/1409.0272/) (2014)
- [Joint Learning of Multiple Differential Networks with fMRI data for Brain Connectivity Alteration Detection](https://mlsanity.com/paper/2106.03334/) (2021)

## Built on by (most cited first, 1 of 1)

- [Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networks](https://mlsanity.com/paper/2301.02423/) (2023)

## Abstract (arXiv)

Major depressive disorder (MDD) requires study of brain functional connectivity alterations for patients, which can be uncovered by resting-state functional magnetic resonance imaging (rs-fMRI) data. We consider the problem of identifying alterations of brain functional connectivity for a single MDD patient. This is particularly difficult since the amount of data collected during an fMRI scan is too limited to provide sufficient information for individual analysis. Additionally, rs-fMRI data usually has the characteristics of incompleteness, sparsity, variability, high dimensionality and high noise. To address these problems, we proposed a multitask Gaussian Bayesian network (MTGBN) framework capable for identifying individual disease-induced alterations for MDD patients. We assume that such disease-induced alterations show some degrees of similarity with the tool to learn such network structures from observations to understanding of how system are structured jointly from related tasks. First, we treat each patient in a class of observation as a task and then learn the Gaussian Bayesian networks (GBNs) of this data class by learning from all tasks that share a default covariance matrix that encodes prior knowledge. This setting can help us to learn more information from limited data. Next, we derive a closed-form formula of the complete likelihood function and use the Monte-Carlo Expectation-Maximization(MCEM) algorithm to search for the approximately best Bayesian network structures efficiently. Finally, we assess the performance of our methods with simulated and real-world rs-fMRI data.