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A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes
The Gaussian process (GP) is a popular statistical technique for stochastic function approximation and uncertainty quantification from data.
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels
A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation in science and engineering applications.
Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processes
Gaussian process regression is a widely-applied method for function approximation and uncertainty quantification.
Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels
A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries.
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