Add to ORKGA method for Gaussian process learning of a scalar function from a set of pair-wise order relationships is presented. Expectation propagation is used to obtain an approximation to the log marginal likelihood which is optimised using an analytical expression for its gra-dient. Experimental results show that the proposed method performs well compared with a previous method for Gaussian process preference learning. 1
Gaussian processes are attractive models for probabilistic classification but unfortunately exact in...
Rich and complex time-series data, such as those generated from engineering systems, financial marke...
Due to their flexible nonparametric nature, Gaussian process models are very effective at solving ha...
A method for Gaussian process learning of a scalar function from a set of pair-wise order relationsh...
Gaussian processes are attractive models for probabilistic classification but unfortunately exact in...
In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian...
Variational methods have been recently considered for scaling the training process of Gaussian proce...
We give a basic introduction to Gaussian Process regression models. We focus on understanding the ro...
A method for large scale Gaussian process classification has been recently proposed based on expecta...
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, p...
Gaussian process (GP) is a stochastic process that has been studied for a long time and gained wide ...
We propose a decision-theoretic sparsification method for Gaussian process preference learning. This...
Rich and complex time-series data, such as those generated from engineering systems, financial marke...
Gaussian process priors can be used to define flexible, probabilistic classification models. Unfortu...
Gaussian Process Preference Learning (GPPL) is considered to be the state-of-the-art algorithm for l...
Gaussian processes are attractive models for probabilistic classification but unfortunately exact in...
Rich and complex time-series data, such as those generated from engineering systems, financial marke...
Due to their flexible nonparametric nature, Gaussian process models are very effective at solving ha...
A method for Gaussian process learning of a scalar function from a set of pair-wise order relationsh...
Gaussian processes are attractive models for probabilistic classification but unfortunately exact in...
In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian...
Variational methods have been recently considered for scaling the training process of Gaussian proce...
We give a basic introduction to Gaussian Process regression models. We focus on understanding the ro...
A method for large scale Gaussian process classification has been recently proposed based on expecta...
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, p...
Gaussian process (GP) is a stochastic process that has been studied for a long time and gained wide ...
We propose a decision-theoretic sparsification method for Gaussian process preference learning. This...
Rich and complex time-series data, such as those generated from engineering systems, financial marke...
Gaussian process priors can be used to define flexible, probabilistic classification models. Unfortu...
Gaussian Process Preference Learning (GPPL) is considered to be the state-of-the-art algorithm for l...
Gaussian processes are attractive models for probabilistic classification but unfortunately exact in...
Rich and complex time-series data, such as those generated from engineering systems, financial marke...
Due to their flexible nonparametric nature, Gaussian process models are very effective at solving ha...