Inverse statistical learning

In the problem of risk minimization with noisy data, we provide excess risk bounds with fast rates for an empirical risk minimization based on a deconvolution kernel.. The risk of

En effet, le droit au respect de la vie privée garanti par l’article  8 de la Convention européenne des Droits de l’Homme avait été défini par l’Assemblée en janvier 1970

For this class of losses, we provide a bias-variance decomposition and show that the assumptions commonly made in least-squares regression, such as the source and capacity

To obtain the minimax rate of convergence of an estimator of θ in a semiparametric setting is the first goal of this note. A lower bound is obtained in Section 2 and an upper bound

This was a key step to compute the derivative of the proximal operator associated to the nuclear norm, and finally to use the SURE to recursively estimate the quadratic prediction

We can see that the difference of the ”true” risk of the randomized estimators ˜ θ and ˜ θ ′ , drawn independently from ρ and ρ ′ , is upper bounded by the difference of

Although statistical learning theory mainly focuses on establishing universal rate bounds (i.e., which hold for any distribution of the data) for the accuracy of a decision rule

Given n independent random vectors with common density f on R d , we study the weak convergence of three empirical-measure based estimators of the convex λ-level set L λ of f ,