MagmaClustR - Clustering and Prediction using Multi-Task Gaussian Processes
with Common Mean
An implementation for the multi-task Gaussian processes
with common mean framework. Two main algorithms, called 'Magma'
and 'MagmaClust', are available to perform predictions for
supervised learning problems, in particular for time series or
any functional/continuous data applications. The corresponding
articles has been respectively proposed by Arthur Leroy, Pierre
Latouche, Benjamin Guedj and Servane Gey (2022)
<doi:10.1007/s10994-022-06172-1>, and Arthur Leroy, Pierre
Latouche, Benjamin Guedj and Servane Gey (2023)
<https://jmlr.org/papers/v24/20-1321.html>. Theses approaches
leverage the learning of cluster-specific mean processes, which
are common across similar tasks, to provide enhanced prediction
performances (even far from data) at a linear computational
cost (in the number of tasks). 'MagmaClust' is a
generalisation of 'Magma' where the tasks are simultaneously
clustered into groups, each being associated to a specific mean
process. User-oriented functions in the package are decomposed
into training, prediction and plotting functions. Some basic
features (classic kernels, training, prediction) of standard
Gaussian processes are also implemented.
