Relate and Predict: Structure-Aware Prediction with Jointly Optimized Neural DAG
This addresses the need for interpretable models that can disentangle sparse feature relationships in prediction tasks, though it appears incremental as it builds on existing deep learning methods by adding structure-aware components.
The paper tackled the problem of modeling explicit feature dependencies for prediction by proposing dGAP, a deep neural network framework that jointly learns neural dependency graphs and optimizes structure-aware target prediction, resulting in improved accuracy and recovery of correct dependency structures on simulated and real datasets.
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.