53 citations · 188 across the 20 of their papers we have counts for
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Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
Mark D. Risser, Marcus M. Noack, Hengrui Luo +1
The Gaussian process (GP) is a widely used probabilistic machine learning method with implicit uncertainty characterization for stochastic function approximation, stochastic modeli…
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes
Marcus M. Noack, Hengrui Luo, Mark D. Risser
The Gaussian process (GP) is a popular statistical technique for stochastic function approximation and uncertainty quantification from data. GPs have been adopted into the realm of…
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels
Marcus M. Noack, Harinarayan Krishnan, Mark D. Risser +1
A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation in science and engineering applications. This success is largely attributed to t…