  We present a multi-task learning formulation for Deep Gaussian processes
(DGPs), through non-linear mixtures of latent processes. The latent space is
composed of private processes that capture within-task information and shared
processes that capture across-task dependencies. We propose two different
methods for segmenting the latent space: through hard coding shared and
task-specific processes or through soft sharing with Automatic Relevance
Determination kernels. We show that our formulation is able to improve the
learning performance and transfer information between the tasks, outperforming
other probabilistic multi-task learning models across real-world and
benchmarking settings.
