260 citations · 738 across the 11 of their papers we have counts for
11 papers
Risk bounds for PU learning under Selected At Random assumption
Olivier Coudray, Christine Keribin, Pascal Massart +1
Positive-unlabeled learning (PU learning) is known as a special case of semi-supervised binary classification where only a fraction of positive examples are labeled. The challenge…
Numerical performance of Penalized Comparison to Overfitting for multivariate kernel density estimation
Suzanne Varet, Claire Lacour, Pascal Massart +1
Kernel density estimation is a well known method involving a smoothing parameter (the bandwidth) that needs to be tuned by the user. Although this method has been widely used the b…
Estimator selection: a new method with applications to kernel density estimation
Claire Lacour, Pascal Massart, Vincent Rivoirard
Estimator selection has become a crucial issue in non parametric estimation. Two widely used methods are penalized empirical risk minimization (such as penalized log-likelihood est…
A new V-fold type procedure based on robust tests
Lucien Birgé, Nelo Magalhães, Pascal Massart
We define a general V-fold cross-validation type method based on robust tests, which is an extension of the hold-out defined by Birg{é} [7, Section 9]. We give some theoretical res…
Rates of convergence for robust geometric inference
Frédéric Chazal, Pascal Massart, Bertrand Michel
Distances to compact sets are widely used in the field of Topological Data Analysis for inferring geometric and topological features from point clouds. In this context, the distanc…
Minimal penalty for Goldenshluger-Lepski method
Claire Lacour, Pascal Massart
This paper is concerned with adaptive nonparametric estimation using the Goldenshluger-Lepski selection method. This estimator selection method is based on pairwise comparisons bet…