A 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 gradient. Experimental results show that the proposed method performs well compared with a previous method for Gaussian process preference learning.
['Ruixuan Wang', 'Stephen J. McKenna']
Gaussian Process Learning from Order Relationships Using Expectation Propagation