Titre :

Cluster-Specific Predictions with Multi-Task Gaussian Processes

Auteur(s) :

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

Titre de la revue :

Journal of Machine Learning Research

Pagination :

1-49

Éditeur :

Microtome Publishing

Date de publication :

2023-01-02

ISSN :

1532-4435

Mot(s)-clé(s) en anglais :

Gaussian processes mixture
curve clustering
multi-task learning
variational EM
cluster-specific predictions

Discipline(s) HAL :

Informatique [cs]/Apprentissage [cs.LG]
Statistiques [stat]/Calcul [stat.CO]
Statistiques [stat]/Théorie [stat.TH]
Statistiques [stat]/Machine Learning [stat.ML]

Résumé en anglais : [en]

A model involving Gaussian processes (GPs) is introduced to simultaneously handle multi-task learning, clustering, and prediction for multiple functional data. This procedure acts as a model-based clustering method for ...
Lire la suite >A model involving Gaussian processes (GPs) is introduced to simultaneously handle multi-task learning, clustering, and prediction for multiple functional data. This procedure acts as a model-based clustering method for functional data as well as a learning step for subsequent predictions for new tasks. The model is instantiated as a mixture of multi-task GPs with common mean processes. A variational EM algorithm is derived for dealing with the optimisation of the hyper-parameters along with the hyper-posteriors' estimation of latent variables and processes. We establish explicit formulas for integrating the mean processes and the latent clustering variables within a predictive distribution, accounting for uncertainty in both aspects. This distribution is defined as a mixture of cluster-specific GP predictions, which enhances the performance when dealing with group-structured data. The model handles irregular grids of observations and offers different hypotheses on the covariance structure for sharing additional information across tasks. The performances on both clustering and prediction tasks are assessed through various simulated scenarios and real datasets. The overall algorithm, called MagmaClust, is publicly available as an R package.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet ANR :

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

Commentaire :

47 pages

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL