PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

Abstract

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5485
Deposited By:Novi Quadrianto
Deposited On:02 November 2009