@inproceedings{shrivastava2022uglad,
author = {Shrivastava, Harsh and Chajewska, Urszula and Abraham, Robin and Chen, Xinshi},
title = {uGLAD: A Deep Learning Model to Recover Conditional Independence Graphs},
booktitle = {Workshop on New Frontiers in Graph Learning (NeurIPS 2022)},
year = {2022},
month = {December},
abstract = {Probabilistic Graphical Models are generative models of complex systems. They rely on conditional independence assumptions between variables to learn sparse representations which can be visualized in a form of a graph. Such models are used for domain exploration and structure discovery in poorly understood domains. This work introduces a novel technique to perform sparse graph recovery by optimizing deep unrolled networks. Assuming that the input data comes from an underlying multivariate Gaussian distribution, we apply a deep model on that outputs the precision matrix. Then, the partial correlation matrix is calculated which can also be interpreted as providing a list of conditional independence assertions holding in the input distribution. Our model, uGLAD, builds upon and extends the state-of-the-art model GLAD to the unsupervised setting. The key benefits of our model are (1) uGLAD automatically optimizes sparsity-related regularization parameters leading to better performance than existing algorithms. (2) We introduce multi-task learning based `consensus' strategy for robust handling of missing data in an unsupervised setting. We evaluate performance on synthetic Gaussian, non-Gaussian data generated from Gene Regulatory Networks, and present case studies in anaerobic digestion and infant mortality.

Software: uGLAD

Additional discussions: Neurips Talk, Tech Blog},
url = {http://approjects.co.za/?big=en-us/research/publication/uglad-recover-conditional-independence-graphs/},
}