Abstract
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. The method is shown to be competitive with state of the art methods whilst allowing for the leverage of the full Gaussian process probabilistic framework.
Original language | English |
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Publication status | Published - 27 Jun 2012 |
Event | 29th International Conference on Machine Learning - Ediburgh, United Kingdom Duration: 26 Jun 2012 → 1 Jul 2012 |
Conference
Conference | 29th International Conference on Machine Learning |
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Abbreviated title | ICML 2012 |
Country/Territory | United Kingdom |
City | Ediburgh |
Period | 26/06/12 → 1/07/12 |
Keywords
- Gaussian process
- probablisitc modelling
- decision theory