{"id":899655,"date":"2022-11-19T17:47:49","date_gmt":"2022-11-20T01:47:49","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2024-08-27T14:10:24","modified_gmt":"2024-08-27T21:10:24","slug":"uglad-recover-conditional-independence-graphs","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/uglad-recover-conditional-independence-graphs\/","title":{"rendered":"uGLAD: A Deep Learning Model to Recover Conditional Independence Graphs"},"content":{"rendered":"

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.<\/p>\n

Software: uGLAD (opens in new tab)<\/span><\/a><\/p>\n

Additional discussions: Neurips Talk<\/a>, Tech Blog (opens in new tab)<\/span><\/a><\/p>\n

\"uGLAD

uGLAD’s deep unrolled architecture<\/p><\/div>\n

\"text\"

Parameterized “learnable” Lagrangian constants<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"

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. 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