3 papers
cs.LG2026
ParamBoost: Gradient Boosted Piecewise Cubic Polynomials
Nicolas Salvadé, Tim Hillel
Generalized Additive Models (GAMs) can be used to create non-linear glass-box (i.e. explicitly interpretable) models, where the predictive function is fully observable over the com…
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
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…