Gaussian processes and Gaussian-related stochastic processes have been shown to be powerful tools for stochastic function approximation, uncertainty quantification, and autonomous control of data acquisition.
Even so, they are often criticized for poor approximation performance and scalability in real-life applications.
The reason, however, is often not the method itself but missing flexibility and domain awareness of the underlying prior probability distribution. In this talk, I will start by discussing some recent examples in which GPs were applied
to various approximation and decision-making problems; we will discover, by example, where the challenges, intricacies, and complexities of this methodology lie, and subsequently, how they can be addressed to yield improved performance.
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