Add to ORKGThe kernel function and its hyperparameters are the central model selection choice in a Gaussian proces (Rasmussen and Williams, 2006). Typically, the hyperparameters of the kernel are chosen by maximising the marginal likelihood, an approach known as Type-II maximum likelihood (ML-II). However, ML-II does not account for hyperparameter uncertainty, and it is well-known that this can lead to severely biased estimates and an underestimation of predictive uncertainty. While there are several works which employ a fully Bayesian characterisation of GPs, relatively few propose such approaches for the sparse GPs paradigm. In this work we propose an algorithm for sparse Gaussian process regression which leverages MCMC to sample from the hyperparam...
When the data are sparse, optimization of hyperparameters of the kernel in Gaussian process regressi...
Institute for Adaptive and Neural ComputationNon-parametric models and techniques enjoy a growing po...
In this thesis we address the problems associated to non-conjugate likelihood Gaussian process model...
The application of Gaussian processes (GPs) is limited by the rather slow process of optimizing the ...
Stochastic gradient descent (SGD) and its variants have established themselves as the go-to algorith...
Variational inference techniques based on inducing variables provide an elegant framework for scalab...
Gaussian Process (GP) inference is a probabilistic kernel method where the GP is treated as a latent...
This paper presents a variational Bayesian kernel selection (VBKS) algorithm for sparse Gaussian pro...
Gaussian process (GP) models form a core part of probabilistic machine learning. Considerable resear...
While much research effort has been dedicated to scaling up sparse Gaussian process (GP) models base...
Gaussian process hyperparameter optimization requires linear solves with, and log-determinants of, l...
Gaussian process (GP) models form a core part of probabilistic machine learning. Con-siderable resea...
Gaussian process (GP) models form a core part of probabilistic machine learning. Considerable resear...
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to in...
Non-parametric models and techniques enjoy a growing popularity in the field of machine learning, an...
When the data are sparse, optimization of hyperparameters of the kernel in Gaussian process regressi...
Institute for Adaptive and Neural ComputationNon-parametric models and techniques enjoy a growing po...
In this thesis we address the problems associated to non-conjugate likelihood Gaussian process model...
The application of Gaussian processes (GPs) is limited by the rather slow process of optimizing the ...
Stochastic gradient descent (SGD) and its variants have established themselves as the go-to algorith...
Variational inference techniques based on inducing variables provide an elegant framework for scalab...
Gaussian Process (GP) inference is a probabilistic kernel method where the GP is treated as a latent...
This paper presents a variational Bayesian kernel selection (VBKS) algorithm for sparse Gaussian pro...
Gaussian process (GP) models form a core part of probabilistic machine learning. Considerable resear...
While much research effort has been dedicated to scaling up sparse Gaussian process (GP) models base...
Gaussian process hyperparameter optimization requires linear solves with, and log-determinants of, l...
Gaussian process (GP) models form a core part of probabilistic machine learning. Con-siderable resea...
Gaussian process (GP) models form a core part of probabilistic machine learning. Considerable resear...
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to in...
Non-parametric models and techniques enjoy a growing popularity in the field of machine learning, an...
When the data are sparse, optimization of hyperparameters of the kernel in Gaussian process regressi...
Institute for Adaptive and Neural ComputationNon-parametric models and techniques enjoy a growing po...
In this thesis we address the problems associated to non-conjugate likelihood Gaussian process model...
