gp2Scale uses compactly supported non-stationary kernels to make the Gaussian-process covariance matrix sparse, enabling exact GP inference on 10 million points at supercomputing scale.
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gp2Scale: A Class of Compactly Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data Points
gp2Scale uses compactly supported non-stationary kernels to make the Gaussian-process covariance matrix sparse, enabling exact GP inference on 10 million points at supercomputing scale.