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Online model selection by learning how compositional kernels evolve.

Eura Shin1, Predrag Klasnja2, Susan A Murphy1

  • 1Department of Computer Science, Harvard University.

Transactions on Machine Learning Research
|June 3, 2024
PubMed
Summary
This summary is machine-generated.

We developed a Kernel Evolution Model (KEM) for fast, personalized online kernel selection in mobile health. KEM efficiently manages complexity and stability, ensuring reliable performance with new user data.

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Area of Science:

  • Machine Learning
  • Computational Biology
  • Health Informatics

Background:

  • Personalized learning in mobile health requires efficient online kernel selection.
  • Existing methods lack speed, complexity control, and stability for real-time health applications.

Purpose of the Study:

  • To introduce a novel Kernel Evolution Model (KEM) for online compositional kernel selection.
  • To address the need for rapid, stable, and complexity-aware kernel selection in multi-task Gaussian Process regression for mobile health.

Main Methods:

  • Developed KEM as a generative process for evolving kernel compositions.
  • Managed bias-variance trade-off through online data observation.
  • Learned kernel evolutions from pilot data for rapid selection on new users.

Main Results:

  • KEM reliably selects high-performing kernels across synthetic and real-world datasets.
  • Demonstrated effectiveness on two distinct health datasets.
  • Pilot data enabled efficient kernel selection for new test users.

Conclusions:

  • KEM provides an efficient and reliable solution for online kernel selection in mobile health.
  • The model successfully balances complexity, sparsity, and stability as data accumulates.
  • KEM facilitates personalized learning through rapid, adaptive kernel selection.