  Understanding relationships between feature variables is one important way
humans use to make decisions. However, state-of-the-art deep learning studies
either focus on task-agnostic statistical dependency learning or do not model
explicit feature dependencies during prediction. We propose a deep neural
network framework, dGAP, to learn neural dependency Graph and optimize
structure-Aware target Prediction simultaneously. dGAP trains towards a
structure self-supervision loss and a target prediction loss jointly. Our
method leads to an interpretable model that can disentangle sparse feature
relationships, informing the user how relevant dependencies impact the target
task. We empirically evaluate dGAP on multiple simulated and real datasets.
dGAP is not only more accurate, but can also recover correct dependency
structure.
