Semi-described and semi-supervised learning with Gaussian processes

[edit]

Andreas Damianou, University of Sheffield
Neil D. Lawrence, University of Sheffield

in 31st Conference on Uncertainty in Artificial Intelligence (UAI)

Related Material

Abstract

Propagating input uncertainty through non-linear Gaussian process (GP) mappings is intractable. This hinders the task of training GPs using uncertain and partially observed inputs. In this paper we refer to this task as “semi-described learning”. We then introduce a GP framework that solves both, the semi-described and the semi-supervised learning problems (where missing values occur in the outputs). Auto-regressive state space simulation is also recognised as a special case of semi-described learning. To achieve our goal we develop variational methods for handling semi-described inputs in GPs, and couple them with algorithms that allow for imputing the missing values while treating the uncertainty in a principled, Bayesian manner. Extensive experiments on simulated and real-world data study the problems of iterative forecasting and regression/classification with missing values. The results suggest that the principled propagation of uncertainty stemming from our framework can significantly improve performance in these tasks.


@InProceedings{damianou-semi15,
  title = 	 {Semi-described and semi-supervised learning with {G}aussian processes},
  author = 	 {Andreas Damianou and Neil D. Lawrence},
  booktitle = 	 {31st Conference on Uncertainty in Artificial Intelligence (UAI)},
  year = 	 {2015},
  month = 	 {00},
  edit = 	 {https://github.com/lawrennd//publications/edit/gh-pages/_posts/2015-01-01-damianou-semi15.md},
  url =  	 {http://inverseprobability.com/publications/damianou-semi15.html},
  abstract = 	 {Propagating input uncertainty through non-linear Gaussian process (GP) mappings is intractable. This hinders the task of training GPs using uncertain and partially observed inputs. In this paper we refer to this task as “semi-described learning”. We then introduce a GP framework that solves both, the semi-described and the semi-supervised learning problems (where missing values occur in the outputs). Auto-regressive state space simulation is also recognised as a special case of semi-described learning. To achieve our goal we develop variational methods for handling semi-described inputs in GPs, and couple them with algorithms that allow for imputing the missing values while treating the uncertainty in a principled, Bayesian manner. Extensive experiments on simulated and real-world data study the problems of iterative forecasting and regression/classification with missing values. The results suggest that the principled propagation of uncertainty stemming from our framework can significantly improve performance in these tasks.},
  key = 	 {Damianou:semi15},
  linkpdf = 	 {http://arxiv.org/pdf/1509.01168v1.pdf},
  OPTgroup = 	 {}
 

}
%T Semi-described and semi-supervised learning with Gaussian processes
%A Andreas Damianou and Neil D. Lawrence
%B 
%C 31st Conference on Uncertainty in Artificial Intelligence (UAI)
%D 
%F damianou-semi15	
%P --
%R 
%U http://inverseprobability.com/publications/damianou-semi15.html
%X Propagating input uncertainty through non-linear Gaussian process (GP) mappings is intractable. This hinders the task of training GPs using uncertain and partially observed inputs. In this paper we refer to this task as “semi-described learning”. We then introduce a GP framework that solves both, the semi-described and the semi-supervised learning problems (where missing values occur in the outputs). Auto-regressive state space simulation is also recognised as a special case of semi-described learning. To achieve our goal we develop variational methods for handling semi-described inputs in GPs, and couple them with algorithms that allow for imputing the missing values while treating the uncertainty in a principled, Bayesian manner. Extensive experiments on simulated and real-world data study the problems of iterative forecasting and regression/classification with missing values. The results suggest that the principled propagation of uncertainty stemming from our framework can significantly improve performance in these tasks.
TY  - CPAPER
TI  - Semi-described and semi-supervised learning with Gaussian processes
AU  - Andreas Damianou
AU  - Neil D. Lawrence
BT  - 31st Conference on Uncertainty in Artificial Intelligence (UAI)
PY  - 2015/07/12
DA  - 2015/07/12	
ID  - damianou-semi15	
SP  - 
EP  - 
L1  - http://arxiv.org/pdf/1509.01168v1.pdf
UR  - http://inverseprobability.com/publications/damianou-semi15.html
AB  - Propagating input uncertainty through non-linear Gaussian process (GP) mappings is intractable. This hinders the task of training GPs using uncertain and partially observed inputs. In this paper we refer to this task as “semi-described learning”. We then introduce a GP framework that solves both, the semi-described and the semi-supervised learning problems (where missing values occur in the outputs). Auto-regressive state space simulation is also recognised as a special case of semi-described learning. To achieve our goal we develop variational methods for handling semi-described inputs in GPs, and couple them with algorithms that allow for imputing the missing values while treating the uncertainty in a principled, Bayesian manner. Extensive experiments on simulated and real-world data study the problems of iterative forecasting and regression/classification with missing values. The results suggest that the principled propagation of uncertainty stemming from our framework can significantly improve performance in these tasks.
ER  -

Damianou, A. & Lawrence, N.D.. (2015). Semi-described and semi-supervised learning with Gaussian processes. 31st Conference on Uncertainty in Artificial Intelligence (UAI) :-