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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Variable selection for unsupervised high-dimensional classification.

Classification non supervisée, Clustering, Critère de sélection de modèle non asymptotique, Data-driven non-asymptotic model selection criterion, Finite Gaussian mixture models, Grande dimension, High dimension, Inégalités oracle, L1-regularization, Lasso, Modèles de mélange gaussien, Oracle inequalities, Régularisation l1, Sélection de variables, Variable selection

Learning the intensity of time events with change-points.

Change-points, Counting processes, Oracle inequalities, Total-variation

Learning the Intensity of Time Events With Change-Points.

Change-points, Counting processes, Oracle inequalities, Total-variation

Binarsity: a penalization for one-hot encoded features.

Features binarization, Oracle inequalities, Proximal methods, Supervised learning, Total-variation

Cross-validation and penalization for density estimation.

Adaptative estimation, Density estimation, Estimateurs linéaires, Estimation adaptative, Estimation de densité, Estimator selection, Hellinger loss, Heuristique de pente, Inégalités d'oracle, Linear estimators, Non-parametric statistics, Oracle inequalities, Penalization, Perte Hellinger, Pénalisation, Slope heuristics, Statistical algorithm selection, Statistiques non-paramétriques, Sélection d'estimateur, Sélection d'une méthode d'estimation, T-estimation, V-fold cross-validation, Validation croisée V-fold

Interplay between concentration, complexity and geometry in learning theory with applications to high dimensional data analysis.

Aggregation methods, Apprentissage statistique, Inégalités oracle, Learning theory, Méthodes d'agrégation, Oracle inequalities, Parcimonie, Penalization, Pénalisation, Regularization, Régularisation

Aggregation procedures: optimality and fast rates.

Adaptation, Classification, Density estimation, Dimension reduction, Estimation de densité, Estimation non-paramétrique, Inégalités d'oracle, Minimax rates of convergence, Non-parametric estimation, Optimality, Optimalité, Oracle inequalities, Réduction de dimension, Régression, Vitesses minimax

The dictionary approach for spherical deconvolution.

Calibration, Density deconvolution, Dictionary, Lasso estimate, Oracle inequalities, Second generation wavelets, Sparsity

The dictionary approach for spherical deconvolution.

Calibration, Density deconvolution, Dictionary, Lasso estimate, Oracle inequalities, Second generation wavelets, Sparsity

Statistical Approaches in Learning Theory: boosting and ranking.

$U$-processes, $U$-processus, AUC criterion, Algorithmes de classification, Apprentissage statistique, Approximation stochastique, Classification algorithms, Convex risk minimization, Critère AUC, Fast rates, Inégalités oracles, Minimisation de risques convexes, Oracle inequalities, Statistical learning, Stochastic approximation stochastique, Vitesses rapides

Sparse single-index model.

MCMC, Nonparametric statistics, Oracle inequalities, PAC-Bayesian inequalities, Single-index model, Sparsity

Prediction of time series by statistical learning: general losses and fast rates.

Fast rates, GDP Forecasting, Mixing, Oracle inequalities, PAC-Bayesian bounds, Statistical learning theory, Time series forecasting, Weak-dependence