Titre :

MAGMA: Inference and Prediction using Multi-Task Gaussian Processes with Common Mean

Auteur(s) :

Leroy, Arthur [Auteur]
Department of Computer Science [Sheffield]
Latouche, Pierre [Auteur]
Mathématiques Appliquées Paris 5 [MAP5 - UMR 8145]
Guedj, Benjamin [Auteur]
Department of Computer science [University College of London] [UCL-CS]
Inria-CWI [Inria-CWI]
MOdel for Data Analysis and Learning [MODAL]
The Inria London Programme [Inria-London]
Gey, Servane [Auteur]
Mathématiques Appliquées Paris 5 [MAP5 - UMR 8145]

Titre de la revue :

Machine Learning

Éditeur :

Springer Verlag

Date de publication :

2022-05-06

ISSN :

0885-6125

Mot(s)-clé(s) en anglais :

Multi-task learning
Gaussian process
EM algorithm
Common mean process
Functional data analysis

Discipline(s) HAL :

Statistiques [stat]/Calcul [stat.CO]
Statistiques [stat]/Machine Learning [stat.ML]
Statistiques [stat]/Méthodologie [stat.ME]
Informatique [cs]/Apprentissage [cs.LG]

Résumé en anglais : [en]

A novel multi-task Gaussian process (GP) framework is proposed, by using a common mean process for sharing information across tasks. In particular, we investigate the problem of time series forecasting, with the objective ...
Lire la suite >A novel multi-task Gaussian process (GP) framework is proposed, by using a common mean process for sharing information across tasks. In particular, we investigate the problem of time series forecasting, with the objective to improve multiple-step-ahead predictions. The common mean process is defined as a GP for which the hyper-posterior distribution is tractable. Therefore an EM algorithm is derived for handling both hyper-parameters optimisation and hyper-posterior computation. Unlike previous approaches in the literature, the model fully accounts for uncertainty and can handle irregular grids of observations while maintaining explicit formulations, by modelling the mean process in a unified GP framework. Predictive analytical equations are provided, integrating information shared across tasks through a relevant prior mean. This approach greatly improves the predictive performances, even far from observations, and may reduce significantly the computational complexity compared to traditional multi-task GP models. Our overall algorithm is called MAGMA (standing for Multi tAsk GPs with common MeAn). The quality of the mean process estimation, predictive performances, and comparisons to alternatives are assessed in various simulated scenarios and on real datasets.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Projet ANR :

Apprentissage PAC-bayésien agnostique
Une Perspective PAC-Bayésienne de l'Apprentissage de Représentations

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL