3.5k citations · 4.1k across the 23 of their papers we have counts for
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Aggregation of Multiple Knockoffs
Tuan-Binh Nguyen, Jérôme-Alexis Chevalier, Bertrand Thirion +1
We develop an extension of the Knockoff Inference procedure, introduced by Barber and Candes (2015). This new method, called Aggregation of Multiple Knockoffs (AKO), addresses 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…
Consistent change-point detection with kernels
Damien Garreau, Sylvain Arlot
In this paper we study the kernel change-point algorithm (KCP) proposed by Arlot, Celisse and Harchaoui (2012), which aims at locating an unknown number of change-points in the dis…
Comments on: "A Random Forest Guided Tour" by G. Biau and E. Scornet
Sylvain Arlot, Robin Genuer
This paper is a comment on the survey paper by Biau and Scornet (2016) about random forests. We focus on the problem of quantifying the impact of each ingredient of random forests…
Analysis of purely random forests bias
Sylvain Arlot, Robin Genuer
Random forests are a very effective and commonly used statistical method, but their full theoretical analysis is still an open problem. As a first step, simplified models such as p…