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Étude de classes de noyaux adaptées à la simplification et à l'interprétation...
The framework of this thesis is the approximation of functions for which the value is known at limited number of points. More precisely, we consider here the so-called... -
Covariance kernels for simplified and interpretable modeling. A functional an...
The framework of this thesis is the approximation of functions for which thevalue is known at limited number of points. More precisely, we consider here the... -
Invariances of random fields paths, with applications in Gaussian Process Reg...
We study pathwise invariances of centred random fields that can be controlled through the covariance. A result involving composition operators is obtained in... -
Online Learning with Multiple Operator-valued Kernels
We consider the problem of learning a vector-valued function f in an online learning setting. The function f is assumed to lie in a reproducing Hilbert space of... -
New normality test in high dimension with kernel methods
A new goodness-of-fit test for normality in high-dimension (and Reproducing Kernel Hilbert Space) is proposed. It shares common ideas with the Maximum Mean Discrepancy... -
Multivariate sensitivity analysis to measure global contribution of input fac...
Many dynamic models are used for risk assessment and decision support in ecology and crop science. Such models generate time-dependent model predictions, with time...
