2020/07/21 by Arthur Leroy, Pierre Latouche, Benjamin Guedj +1 · 2 citations
Chemistry · Computer Science · Decision Sciences · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Algorithm #Artificial intelligence #Chemistry #Computation #Computer science #Data mining #Economics #Forecasting Techniques and Applications #Gaussian #Gaussian Processes and Bayesian Inference #Gaussian process #Geology #Inference #Machine learning #Magma #Process (computing) #Seismology #Target Tracking and Data Fusion in Sensor Networks #Task (project management) #Volcano #cs.LG #stat.CO #stat.ME #stat.ML
paper · pdf · doi:10.1007/s10994-022-06172-1
published in arXiv (Cornell University) 111(5), 1821-1849 (Cornell University)
openalex publication_date 2020/07/21 · arxiv created 2022/05/24 · arxiv updated 2022/05/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
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 Gaussian processes 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.