Patrimony

The Louis Bachelier Group's patrimony has been defined as all the publications produced by academic researchers thanks to Group funding (ILB, FdR, IEF, Labex) or via the use of EquipEx data (BEDOFIH, EUROFIDAI).


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Banking risk indicators, machine learning and one-sided concentration inequalities.

Banking Risks, Forêts d'arbres décisionnels, K-means, K-moyennes, Modèle Structurel, Random Forests, Risque systémique, Risques bancaires, Structural Model, Systemic Risk, Unimodality, Unimodalité

New Insights into Decision Trees Ensembles.

Boosting, Discrimination Stochastique, Ensemble methods, Forêts aléatoires, Méthodes ensemblistes, Random Forests, Stochastic Discrimination

Bringing ABC inference to the machine learning realm : AbcRanger, an optimized random forests library for ABC.

Approximate Bayesian Computation, C++, Model Choice, Parameter Estimation, Python, R, Random Forests

Random forest estimation of conditional distribution functions and conditional quantiles.

Conditional distribution functions, Conditional quantiles, Consistency, Random Forests

Statistical learning for wind power: A modeling and stability study towards forecasting.

Bagging, Data mining, Forecasting, Modeling, Random forests, Stability, Wind power

Budget learning based on equivalent trees and genetic algorithm : application to fall detection algorithm embedding.

Budget learning, Equivalent decision trees, Genetic algorithm, Prediction time cost, Random forests

Non-Parametric Methods for Post-Processing of Ensemble Forecasts.

Ensemble forecasting, Extreme events, Forêts aléatoires, Meteorology, Météorologie, Prévision d'ensemble, Quantile regression, Random forests, Régression quantile, Statistics, Statistiques, Verification, Vérification, Événements extrêmes