Kernel conditional quantile estimation via reduction revisited
Novi Quadrianto, Kristian Kersting, Mark Reid, Tiberio Caetano and Wray Buntine
In: IEEE International Conference on Data Mining, 6-9 Dec 2009, Miami, Florida, USA.
Quantile regression refers to the process of estimating
the quantiles of a conditional distribution and has
many important applications within econometrics and data
mining, among other domains. In this paper, we show how
to estimate these conditional quantile functions within a Bayes
risk minimization framework using a Gaussian process prior.
The resulting non-parametric probabilistic model is easy to
implement and allows non-crossing quantile functions to be
enforced. Moreover, it can directly be used in combination
with tools and extensions of standard Gaussian Processes
such as principled hyperparameter estimation, sparsification,
and quantile regression with input-dependent noise rates. No
existing approach enjoys all of these desirable properties.
Experiments on benchmark datasets show that our method
is competitive with state-of-the-art approaches.