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arXiv · 1806.00050

Interpretable Set Functions

Andrew Cotter·Maya Gupta·Heinrich Jiang·James Muller·Taman Narayan·Serena Wang·Tao Zhu
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Abstract

We propose learning flexible but interpretable functions that aggregate a variable-length set of permutation-invariant feature vectors to predict a label. We use a deep lattice network model so we can architect the model structure to enhance interpretability, and add monotonicity constraints between inputs-and-outputs. We then use the proposed set function to automate the engineering of dense, interpretable features from sparse categorical features, which we call semantic feature engine. Experiments on real-world data show the achieved accuracy is similar to deep sets or deep neural networks, and is easier to debug and understand.

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Andrew Cotter, Maya Gupta, Heinrich Jiang, James Muller, Taman Narayan, Serena Wang, Tao Zhu. 2018-05-31. Interpretable Set Functions. https://arxiv.org/abs/1806.00050

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