Interpretable Set Functions

Understand

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.