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Using transformed domains to sparsify Gaussian Processes

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If you have a question about this talk, please contact Zoubin Ghahramani.

One of the main limitations associated to the use of Gaussian Processes (GPs) to model data is their high computational cost, which is O(n^3) for n training samples. In this talk, I will discuss several recently proposed GP models that rely on the use of a transformed domain to achieve sparsity and make computation for large data sets affordable. These proposals are comparable in terms of computational cost to previous approaches, such as the celebrated Sparse Pseudo-Input GP (also known as FITC ), but typically offer superior performance. In particular, I will present the Sparse Spectrum GP (SSGP) as a very fast tool to model stationary processes and the Inter-Domain GP (IDGP) as a general framework for inference that can be used both to understand previous sparse GP models and to design new ones. A variational extension of the latter framework that rigorously addresses the overfilling problem will also be discussed.

This talk is part of the Machine Learning @ CUED series.

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