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arXiv 2103.024052021-03-03cs.LGcs.AIstat.ML

Relate and Predict: Structure-Aware Prediction with Jointly Optimized Neural DAG

University of Virginia（弗吉尼亚大学）

Arshdeep Sekhon, Zhe Wang, Yanjun Qi

备注：: 8 pages, 6 figures, version appeared in ICML Workshop 2020 Graph Representation Learning and Beyond (GRL+)

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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.

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