Online structural kernel selection for mobile health
This work addresses the need for efficient and personalized learning in mobile health, focusing on multi-task settings.
The paper tackles the problem of online kernel selection for Gaussian Process regression in mobile health by proposing a novel generative process on kernel composition, showing that kernel evolution trajectories can be transferred between users to improve learning and that the kernels are meaningful for prediction goals.
Motivated by the need for efficient and personalized learning in mobile health, we investigate the problem of online kernel selection for Gaussian Process regression in the multi-task setting. We propose a novel generative process on the kernel composition for this purpose. Our method demonstrates that trajectories of kernel evolutions can be transferred between users to improve learning and that the kernels themselves are meaningful for an mHealth prediction goal.