QuaKerReiCaeetal10.pdf (457.17 kB)
Kernel conditional quantile estimation via reduction revisited
conference contribution
posted on 2023-06-08, 16:45 authored by Novi QuadriantoNovi Quadrianto, Kristian Kersting, Mark D Reid, Tiberio S Caetano, Wray L BuntineQuantile 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."
History
Publication status
- Published
File Version
- Accepted version
Journal
Proceedings of the 9th IEEE International Conference on Data Mining; Miami, Florida; 6-9 December 2009ISSN
1550-4786Publisher
Institute of Electrical and Electronics EngineersExternal DOI
Page range
938-943Pages
1108.0Book title
The ninth IEEE international conference on data mining: ICDM 2009: Miami, Florida 6– 9 December 2009Place of publication
Los Alamitos, CaliforniaISBN
9781424452422Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Editors
Wei Wang, Philip S Yu, Sanjay Ranka, Hillol Kargupta, Xindong WuLegacy Posted Date
2014-02-24First Open Access (FOA) Date
2021-02-20First Compliant Deposit (FCD) Date
2021-02-20Usage metrics
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