Online structural kernel selection for mobile health

TL;DR

This paper introduces an online kernel selection method for Gaussian Process regression in mobile health, enabling personalized, efficient learning by transferring kernel evolution trajectories between users.

Contribution

It proposes a novel generative process for kernel composition and demonstrates kernel transferability improves multi-user health predictions.

Findings

Kernel trajectories can be transferred between users to enhance learning.

The learned kernels are meaningful for health prediction tasks.

The method improves prediction accuracy in mobile health applications.

Abstract

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.