# Online structural kernel selection for mobile health

arXiv: https://arxiv.org/abs/2107.09949 · HTML: https://mlsanity.com/paper/2107.09949/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 21 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [Structure Discovery in Nonparametric Regression through Compositional Kernel Search](https://mlsanity.com/paper/1302.4922/) (2013)
- [Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity](https://mlsanity.com/paper/1909.03539/) (2019)
- [Automatic Construction and Natural-Language Description of Nonparametric Regression Models](https://mlsanity.com/paper/1402.4304/) (2014)
- [Differentiable Compositional Kernel Learning for Gaussian Processes](https://mlsanity.com/paper/1806.04326/) (2018)
- [Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes](https://mlsanity.com/paper/1706.02524/) (2017)
- [Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior](https://mlsanity.com/paper/2012.11339/) (2020)

## 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.