machine – Page 13 – machine-learning-2020

Gaussian Processes – Iterative Sparse Approximations

Lehel Csato

Flexible and efficient Gaussian process models for machine learning

Edward Lloyd Snelson

NONLINEAR DYNAMICS IDENTIFICATION USING GAUSSIAN PROCESS PRIOR MODELS WITHIN A BAYESIAN CONTEXT

Keith Neo Kian Seng

Bayesian Gaussian Processes for Regression and Classification

Mark N. Gibbs

Keyon Vafa

Training and Inference for Deep Gaussian Processes

Nicolas Durrande’s homepage (kernel based methods for interpolation or approximation problems, both from the Gaussian process and RKHS point of view) Phd Thesis (in French) Phd Thesis (slides in English)

Étude de classes de noyaux adaptées à la simplification et à l’interprétation des modèles d’approximation. Une approche fonctionnelle et probabiliste

Nicolas Durrande

Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quantification for computer experiments

Francois Bachoc