# An Interpretable Determinantal Choice Model for Subset Selection

arXiv: https://arxiv.org/abs/2302.11477 · HTML: https://mlsanity.com/paper/2302.11477/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 22 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [Rank-normalization, folding, and localization: An improved widehatR for assessing convergence of MCMC](https://mlsanity.com/paper/1903.08008/) (2019)
- [Learning the Parameters of Determinantal Point Process Kernels](https://mlsanity.com/paper/1402.4862/) (2014)
- [A Survey on LoRa Networking: Research Problems, Current Solutions and Open Issues](https://mlsanity.com/paper/1908.10195/) (2019)
- [Understanding the limits of LoRaWAN](https://mlsanity.com/paper/1607.08011/) (2016)
- [Low Power Wide Area Network Analysis: Can LoRa Scale?](https://mlsanity.com/paper/1610.04793/) (2016)
- [Determinantal thinning of point processes with network learning applications](https://mlsanity.com/paper/1810.08672/) (2018)

## Built on by (most cited first, 1 of 1)

- [Transformer Choice Net: A Transformer Neural Network for Choice Prediction](https://mlsanity.com/paper/2310.08716/) (2023)

## Abstract (arXiv)

Understanding how subsets of items are chosen from offered sets is critical to assortment planning, wireless network planning, and many other applications. There are two seemingly unrelated subset choice models that capture dependencies between items: intuitive and interpretable random utility models; and tractable determinantal point processes (DPPs). This paper connects the two. First, all DPPs are shown to be random utility models. Next, a determinantal choice model that enjoys the best of both worlds is specified; the model is shown to subsume logistic regression when dependence is minimal, and MNL when dependence is maximally negative. This makes the model interpretable, while retaining the tractability of DPPs. A simulation study verifies that the model can learn a continuum of negative dependencies from data, and an applied study using original experimental data produces novel insights on wireless interference in LoRa networks.