  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.
