Online structural kernel selection for mobile health
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 6 of its 23 references that are in the map. First posted on arXiv 21 Jul 2021.
Builds on
6 of 23 references.
References · 6
- 2013Structure Discovery in Nonparametric Regression through Compositional Kernel Searchcited by 87
- 2019Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activitycited by 70
- 2014Automatic Construction and Natural-Language Description of Nonparametric Regression Modelscited by 40
- 2018Differentiable Compositional Kernel Learning for Gaussian Processescited by 23
- 2017Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processescited by 19
- 2020Learning Compositional Sparse Gaussian Processes with a Shrinkage Priorcited by 1
Built on by
Not cited yet.