Sparse Gaussian Processes

We review the motivation for and implementation of sparse Gaussian processes. Special attention is given to the variational method of Titsias (2009), which addresses many of the shortcomings of the previous state of the art and serves as a foundation for many current extensions.

[Slides]

References:

Titsias, M. "Variational Learning of Inducing Variables in Sparse Gaussian Processes." International Conference on Artificial Intelligence and Statistics (2009): 567-574

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