Semi-described and semi-supervised learning with Gaussian processes

Andreas DamianouNeil D. Lawrence
, 2015.

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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-damianou-semi15, title = {Semi-described and semi-supervised learning with {G}aussian processes}, author = {Andreas Damianou and Neil D. Lawrence}, year = {}, editor = {}, 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.} }
Endnote
%0 Conference Paper %T Semi-described and semi-supervised learning with Gaussian processes %A Andreas Damianou %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-damianou-semi15 %I PMLR %J Proceedings of Machine Learning Research %P -- %U http://inverseprobability.com %V %W PMLR %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.
RIS
TY - CPAPER TI - Semi-described and semi-supervised learning with Gaussian processes AU - Andreas Damianou AU - Neil D. Lawrence BT - PY - DA - ED - ID - pmlr-v-damianou-semi15 PB - PMLR SP - DP - PMLR EP - L1 - 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 -
APA
Damianou, A. & Lawrence, N.D.. (). Semi-described and semi-supervised learning with Gaussian processes. , in PMLR :-

Related Material