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Online structural kernel selection for mobile health
Eura Shin · Predag Klasnja · Susan Murphy · Finale Doshi-Velez

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 the mHealth prediction goal.

Author Information

Eura Shin (Harvard University)
Predag Klasnja
Susan Murphy (Harvard University)
Finale Doshi-Velez (Harvard University)

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