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Multi-task/Multi-output learning seeks to exploit correlation among tasks toenhance performance over learning or solving each task independently. In thispaper, we investigate this problem in the context of Gaussian Processes (GPs)and propose a new model which learns a mixture of latent processes bydecomposing the covariance matrix into a sum of structured hidden componentseach of which is controlled by a latent GP over input features and a "weight"over tasks. From this sum structure, we propose a parallelizable parameterlearning algorithm with a predetermined initialization for the "weights". Wealso notice that an ensemble parameter learning approach using mini-batches oftraining data not only reduces the computation complexity of learning but alsoimproves the regression performance. We evaluate our model on two datasets, thesmaller Swiss Jura dataset and another relatively larger ATMS dataset fromNOAA. Substantial improvements are observed compared with establishedalternatives.

Ensemble Multi-task Gaussian Process Regression with Multiple Latent Processes