# Gaussian Process Latent Class Choice Models

arXiv: https://arxiv.org/abs/2101.12252 · HTML: https://mlsanity.com/paper/2101.12252/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 109 references that are in the map.
- First posted on arXiv 28 Jan 2021.

## Builds on (most cited first, 16 of 16)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [Automatic Construction and Natural-Language Description of Nonparametric Regression Models](https://mlsanity.com/paper/1402.4304/) (2014)
- [Inferring transportation modes from GPS trajectories using a convolutional neural network](https://mlsanity.com/paper/1804.02386/) (2018)
- [Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation](https://mlsanity.com/paper/1812.08739/) (2018)
- [Generalized Multi-Output Gaussian Process Censored Regression](https://mlsanity.com/paper/2009.04822/) (2020)
- [Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data](https://mlsanity.com/paper/1812.08733/) (2018)
- [Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models](https://mlsanity.com/paper/1906.03855/) (2019)
- [Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling](https://mlsanity.com/paper/1706.00505/) (2017)
- [A Bayesian Additive Model for Understanding Public Transport Usage in Special Events](https://mlsanity.com/paper/1812.08755/) (2018)
- [Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based Evaluations](https://mlsanity.com/paper/1904.03647/) (2019)
- [A Dirichlet Process Mixture Model of Discrete Choice](https://mlsanity.com/paper/1801.06296/) (2018)
- [Semi-nonparametric Latent Class Choice Model with a Flexible Class Membership Component: A Mixture Model Approach](https://mlsanity.com/paper/2007.02739/) (2020)
- [Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data](https://mlsanity.com/paper/1901.00227/) (2019)

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

- [Attitudes and Latent Class Choice Models using Machine learning](https://mlsanity.com/paper/2302.09871/) (2023)

## Abstract (arXiv)

We present a Gaussian Process - Latent Class Choice Model (GP-LCCM) to integrate a non-parametric class of probabilistic machine learning within discrete choice models (DCMs). Gaussian Processes (GPs) are kernel-based algorithms that incorporate expert knowledge by assuming priors over latent functions rather than priors over parameters, which makes them more flexible in addressing nonlinear problems. By integrating a Gaussian Process within a LCCM structure, we aim at improving discrete representations of unobserved heterogeneity. The proposed model would assign individuals probabilistically to behaviorally homogeneous clusters (latent classes) using GPs and simultaneously estimate class-specific choice models by relying on random utility models. Furthermore, we derive and implement an Expectation-Maximization (EM) algorithm to jointly estimate/infer the hyperparameters of the GP kernel function and the class-specific choice parameters by relying on a Laplace approximation and gradient-based numerical optimization methods, respectively. The model is tested on two different mode choice applications and compared against different LCCM benchmarks. Results show that GP-LCCM allows for a more complex and flexible representation of heterogeneity and improves both in-sample fit and out-of-sample predictive power. Moreover, behavioral and economic interpretability is maintained at the class-specific choice model level while local interpretation of the latent classes can still be achieved, although the non-parametric characteristic of GPs lessens the transparency of the model.