Numerical data imputation algorithms consist in replacing missing values by estimates to allow extensive use of incomplete datasets. Current imputation methods seek to minimize the error between the unobserved ground truth and the imputed values. We will see how this strategy can create (...)

Classical false discovery rate (FDR) controlling procedures offer strong and interpretable guarantees but often lack flexibility to work with complex data. By contrast, machine learning-based classification algorithms have superior performances on modern datasets but typically fall short of (...)

In this talk I will present a spatial Pareto maximization problem which takes transport costs into account. The existence of an integrable equilibrium distribution of goods is non trivial and will be presented. Duality techniques will help us to establish a strong duality result which can be (...)

Subsampling is the cornerstone of approximation theory. This paradigm has many applications in data analysis, signal processing, machine learning, and statistics. Recently, many works tackled the use of kernel-based approximations in these fields. In a nutshell, a kernel-based approximation (...)

Bien que l’utilisation des estimateurs à noyau soit très répandue, la sélection du paramètre de lissage (la fenêtre) demeure un défi pour combiner à la fois efficacité algorithmique et pertinence statistique. En particulier, les performances théoriques et numériques de ces estimateurs dépendent fortement (...)