Gaussian Process Quantile Regression using Expectation Propagation

Understand

Direct quantile regression involves estimating a given quantile of a response variable as a function of input variables.

• We present a new framework for direct quantile regression where a Gaussian process model is learned, minimising the expected tilted loss function.
• The integration required in learning is not analytically tractable so to speed up the learning we employ the Expectation Propagation algorithm.
• We describe how this work relates to other quantile regression methods and apply the method on both synthetic and real data sets.