Online structural kernel selection for mobile health

07/21/2021
by   Eura Shin, et al.
5

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/07/2017

Multiresolution Kernel Approximation for Gaussian Process Regression

Gaussian process regression generally does not scale to beyond a few tho...
research
12/21/2020

Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior

Choosing a proper set of kernel functions is an important problem in lea...
research
10/24/2011

Multiple Gaussian Process Models

We consider a Gaussian process formulation of the multiple kernel learni...
research
06/03/2022

Learning "best" kernels from data in Gaussian process regression. With application to aerodynamics

This paper introduces algorithms to select/design kernels in Gaussian pr...
research
10/11/2019

Evolving Gaussian Process kernels from elementary mathematical expressions

Choosing the most adequate kernel is crucial in many Machine Learning ap...
research
09/13/2023

Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models

In order for robots to safely navigate in unseen scenarios using learnin...
research
08/21/2018

Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals

Currently there is no validated objective measure of pain. Recent neuroi...

Please sign up or login with your details

Forgot password? Click here to reset