Splitting gaussian processes for computationally-efficient regression

HIGHLIGHTS

  • who: Nick Terry and Youngjun Choe from the Department of Industrial and Systems Engineering, University of Washington, Seattle, WA, United States of have published the Article: Splitting Gaussian processes for computationally-efficient regression, in the Journal: PLOS ONE of August/24,/2021
  • what: The authors propose an algorithm for sequentially partitioning the input space and fitting a localized Gaussian process to each disjoint region. The authors demonstrate the efficacy of the resulting model on several multi-dimensional regression tasks. A software implementation of the algorithm is provided, which leverages the computational advantages of the GPyTorch . . .

     

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