# Interpretable Set Functions

arXiv: https://arxiv.org/abs/1806.00050 · HTML: https://mlsanity.com/paper/1806.00050/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2018.

## Built on by (most cited first, 2 of 2)

- [On permutation-invariant neural networks](https://mlsanity.com/paper/2403.17410/) (2024)
- [Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction](https://mlsanity.com/paper/2104.08874/) (2021)

## Abstract (arXiv)

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.