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PhD Thesis

Nonlinear Modelling and Control using Gaussian Processes

Andrew McHutchon

NONSTATIONARY GAUSSIAN PROCESSES FOR REGRESSION AND SPATIAL MODELLING

Christopher Joseph Paciorek

Automatic Model Construction with Gaussian Processes

David Kristjanson Duvenaud

Training and Inference for Deep Gaussian Processes

Keyon Vafa

Bayesian Time Series Learning with Gaussian Processes

Roger Frigola-Alcalde

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

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

Nicolas Durrande

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