output
20142017
most citedOptimization by gradient boosting

6 citations

researchers with a paper here
collaborating institutions

6 papers

math.ST2017★ 6 cited

Optimization by gradient boosting

Gérard Biau, Benoît Cadre

Gradient boosting is a state-of-the-art prediction technique that sequentially produces a model in the form of linear combinations of simple predictors---typically decision trees--…

cs.RO2016

How do walkers avoid a mobile robot crossing their way?

Christian Vassallo, Anne-Hélène Olivier, Philippe Souères +3

Robots and Humans have to share the same environment more and more often. In the aim of steering robots in a safe and convenient manner among humans it is required to understand ho…

math.OC2015

Estimates of First and Second Order Shape Derivatives in Nonsmooth Multidimensional Domains and Applications

Jimmy Lamboley, Arian Novruzi, Michel Pierre

In this paper we investigate continuity properties of first and second order shape derivatives of functionals depending on second order elliptic PDE's around nonsmooth domains, ess…

math.ST2014

Cox process functional learning

Gérard Biau, Benoît Cadre, Quentin Paris

This article addresses the problem of functional supervised classification of Cox process trajectories, whose random intensity is driven by some exogenous random covariable. The cl…

math.AP2014

Diffusion limit for the radiative transfer equation perturbed by a Markovian process

Arnaud Debussche, Sylvain De Moor, Julien Vovelle

We study the stochastic diffusive limit of a kinetic radiative transfer equation, which is non-linear, involving a small parameter and perturbed by a smooth random term. Under an a…

math.AP2014

Diffusion limit for the radiative transfer equation perturbed by a Wiener process

Arnaud Debussche, Sylvain De Moor, Julien Vovelle

The aim of this paper is the rigorous derivation of a stochastic non-linear diffusion equation from a radiative transfer equation perturbed with a random noise. The proof of the co…