Search Results for author: Jean-Jil Duchamps

Found 2 papers, 0 papers with code

Infinitesimal gradient boosting

no code implementations26 Apr 2021 Clément Dombry, Jean-Jil Duchamps

We define infinitesimal gradient boosting as a limit of the popular tree-based gradient boosting algorithm from machine learning.

From individual-based epidemic models to McKendrick-von Foerster PDEs: A guide to modeling and inferring COVID-19 dynamics

no code implementations19 Jul 2020 Félix Foutel-Rodier, François Blanquart, Philibert Courau, Peter Czuppon, Jean-Jil Duchamps, Jasmine Gamblin, Élise Kerdoncuff, Rob Kulathinal, Léo Régnier, Laura Vuduc, Amaury Lambert, Emmanuel Schertzer

We present a unifying, tractable approach for studying the spread of viruses causing complex diseases requiring to be modeled using a large number of types (e. g., infective stage, clinical state, risk factor class).

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