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Jacob R. Gardner

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Research Papers

Exact Gaussian Processes on a Million Data Points

K. A. Wang, G. Pleiss, J. R. Gardner, S. Tyree, K. Q. Weinberger, A. G. Wilson
Gaussian processes (GPs) are flexible models with state-of-the-art performance on many impactful applications. However, computational constraints with standard inference procedures have limited exact GPs to problems with fewer than about ten thousand training points, necessitating approximations for larger datasets. In this paper, we develop a scalable approach for exact GPs that leverages multi-GPU parallelization and methods like linear conjugate gradients, accessing the kernel matrix only through matrix multiplication. [...] [PDF]
arXiv, 2019

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