16 citations · 33 across the 6 of their papers we have counts for
8 papers
A Data Fusion Approach for Ride-sourcing Demand Estimation: A Discrete Choice Model with Sampling and Endogeneity Corrections
Rico Krueger, Michel Bierlaire, Prateek Bansal
Ride-sourcing services offered by companies like Uber and Didi have grown rapidly in the last decade. Understanding the demand for these services is essential for planning and mana…
ciDATGAN: Conditional Inputs for Tabular GANs
Gael Lederrey, Tim Hillel, Michel Bierlaire
Conditionality has become a core component for Generative Adversarial Networks (GANs) for generating synthetic images. GANs are usually using latent conditionality to control the g…
DATGAN: Integrating expert knowledge into deep learning for synthetic tabular data
Gael Lederrey, Tim Hillel, Michel Bierlaire
Synthetic data can be used in various applications, such as correcting bias datasets or replacing scarce original data for simulation purposes. Generative Adversarial Networks (GAN…
Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms
Gael Lederrey, Virginie Lurkin, Tim Hillel +1
The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of da…
Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models
Filipe Rodrigues, Nicola Ortelli, Michel Bierlaire +1
Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying t…
Variational Bayesian Inference for Mixed Logit Models with Unobserved Inter- and Intra-Individual Heterogeneity
Rico Krueger, Prateek Bansal, Michel Bierlaire +2
Variational Bayes (VB), a method originating from machine learning, enables fast and scalable estimation of complex probabilistic models. Thus far, applications of VB in discrete c…