2 citations · 3 across the 7 of their papers we have counts for
8 papers
Concentration of the bootstrap empirical process, with applications to statistical inference
Guillaume Maillard, Adrien Saumard
Considering a general framework of bootstrap with exchangeable weights, we show some concentration inequalities for the supremum of the bootstrap empirical process. On the one hand…
Robust density estimation with the -loss. Applications to the estimation of a density on the line satisfying a shape constraint
Y. Baraud, H. Halconruy, G. Maillard
We solve the problem of estimating the distribution of presumed i.i.d. observations for the total variation loss. Our approach is based on density models and is versatile enough to…
Local asymptotics of cross-validation in least-squares density estimation
Guillaume Maillard
In model selection, several types of cross-validation are commonly used and many variants have been introduced. While consistency of some of these methods has been proven, their ra…
Aggregated hold out for sparse linear regression with a robust loss function
Guillaume Maillard
Sparse linear regression methods generally have a free hyperparameter which controls the amount of sparsity, and is subject to a bias-variance tradeoff. This article considers the…
Aggregated Hold-Out
Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle
Aggregated hold-out (Agghoo) is a method which averages learning rules selected by hold-out (that is, cross-validation with a single split). We provide the first theoretical guaran…
Cross-validation improved by aggregation: Agghoo
Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle
Cross-validation is widely used for selecting among a family of learning rules. This paper studies a related method, called aggregated hold-out (Agghoo), which mixes cross-validati…