37 citations · 101 across the 12 of their papers we have counts for
12 papers
An instance-based learning approach for evaluating the perception of ride-hailing waiting time variability
Nejc Geržinič, Oded Cats, Niels van Oort +3
Understanding user's perception of service variability is essential to discern their overall perception of any type of (transport) service. We study the perception of waiting time…
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…
Robust discrete choice models with t-distributed kernel errors
Rico Krueger, Michel Bierlaire, Thomas Gasos +1
Outliers in discrete choice response data may result from misclassification and misreporting of the response variable and from choice behaviour that is inconsistent with modelling…
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…
Pólygamma Data Augmentation to address Non-conjugacy in the Bayesian Estimation of Mixed Multinomial Logit Models
Prateek Bansal, Rico Krueger, Michel Bierlaire +2
The standard Gibbs sampler of Mixed Multinomial Logit (MMNL) models involves sampling from conditional densities of utility parameters using Metropolis-Hastings (MH) algorithm due…
Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based Evaluations
Prateek Bansal, Rico Krueger, Michel Bierlaire +2
Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of mixed…
Dimension Reduction for Origin-Destination Flow Estimation: Blind Estimation Made Possible
Jingyuan Xia, Wei Dai, John Polak +1
This paper studies the problem of estimating origin-destination (OD) flows from link flows. As the number of link flows is typically much less than that of OD flows, the inverse pr…
The robust single machine scheduling problem with uncertain release and processing times
Nitish Umang, Alan L. Erera, Michel Bierlaire
In this work, we study the single machine scheduling problem with uncertain release times and processing times of jobs. We adopt a robust scheduling approach, in which the measure…