Gaussian process product models for nonparametric nonstationarity
By Ryan P. Adams, Oliver Stegle. 2008
54 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Zoubin Ghahramani
- Andrew Gordon Wilson
- Frank Hutter
- Nando de Freitas
- Ruslan Salakhutdinov
- Yoshua Bengio
- Ryan P. Adams
In Computational Theory and Mathematics
- Nando de Freitas
- Frank Hutter
- Ziyun Wang
- Andrew Gordon Wilson
- Zoubin Ghahramani
- Bobak Shahriari
- Ryan P. Adams
- Kevin Swersky
In Control and Systems Engineering
- Ryan P. Adams
- Andrew Gordon Wilson
- Zoubin Ghahramani
- David A. Knowles
- Nando de Freitas
- Kevin Swersky
- Satish T. S. Bukkapatnam
- Ziyun Wang
Citations in the corpus
- Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, background): “Another form of nonstationarity that is closely related to heteroscedasticity is a nonstationary amplitude [1], [54].”
- Gaussian Process Regression Networks (2011, methodology): “…been great interest in extending Gaussian process (GP) regression to account for input dependent noise variances (Goldberg et al., 1998; Kersting et al., 2007; Adams and Stegle, 2008; Turner and Sahani, 2008; Turner, 2010; Wilson and Ghahramani, 2010a,b; L´azaro-Gredilla and Titsias, 2011).”
- Heteroscedastic Treed Bayesian Optimisation (2014, background): “Warping is another popular approach for dealing with nonstationarity (Snelson et al., 2004; Adams and Stegle, 2008).”
- Time series forecasting for nonlinear and non-stationary processes: a review and comparative study (2015, methodology): “The GP model of Adams and Stegle (2008) treats data as a point-wise product of two GPs to handle non-stationarity.”