activity
20182020
most citedFeature quantization for parsimonious and interpretable predictive models

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

stat.ME2020

Simultaneous semi-parametric estimation of clustering and regression

Matthieu Marbac, Mohammed Sedki, Christophe Biernacki +1

We investigate the parameter estimation of regression models with fixed group effects, when the group variable is missing while group related variables are available. This problem…

stat.AP2020

An end-to-end data-driven optimisation framework for constrained trajectories

Florent Dewez, Benjamin Guedj, Arthur Talpaert +1

Many real-world problems require to optimise trajectories under constraints. Classical approaches are based on optimal control methods but require an exact knowledge of the underly…

stat.AP2020

From industry-wide parameters to aircraft-centric on-flight inference: improving aeronautics performance prediction with machine learning

Florent Dewez, Benjamin Guedj, Vincent Vandewalle

Aircraft performance models play a key role in airline operations, especially in planning a fuel-efficient flight. In practice, manufacturers provide guidelines which are slightly…

stat.ME20191 cited

Feature quantization for parsimonious and interpretable predictive models

Adrien Ehrhardt, Christophe Biernacki, Vincent Vandewalle +1

For regulatory and interpretability reasons, logistic regression is still widely used. To improve prediction accuracy and interpretability, a preprocessing step quantizing both con…

stat.ME2018

A tractable Multi-Partitions Clustering

Matthieu Marbac, Vincent Vandewalle

In the framework of model-based clustering, a model allowing several latent class variables is proposed. This model assumes that the distribution of the observed data can be factor…