8 citations · 8 across the 2 of their papers we have counts for
2 papers
stat.ML2025
Functional effects models: Accounting for preference heterogeneity in panel data with machine learning
Nicolas Salvadé, Tim Hillel
In this paper, we present a general specification for Functional Effects Models, which use Machine Learning (ML) methodologies to learn individual-specific preference parameters fr…
cs.LG2024★ 8 cited
RUMBoost: Gradient Boosted Random Utility Models
Nicolas Salvadé, Tim Hillel
This paper introduces the RUMBoost model, a novel discrete choice modelling approach that combines the interpretability and behavioural robustness of Random Utility Models (RUMs) w…