University of Cambridge > Talks.cam > Machine Learning @ CUED > Variational Fourier Features For Gaussian Process - Joint work with Nicolas Durrande and Arno Solin

Variational Fourier Features For Gaussian Process - Joint work with Nicolas Durrande and Arno Solin

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This work brings together two powerful concepts in Gaussian processes: the variational approach to sparse approximation and the spectral representation of Gaussian processes. This gives rise to an approximation that inherits the benefits of the variational approach but with the representational power and computational scalability of spectral representations. The work hinges on a key result that there exists spectral features related to a finite domain of the Gaussian process which exhibit almost-independent covariance. Under the assumption of additive Gaussian noise, our method requires only a single pass through the dataset, making for very fast and accurate computation: we compute on the N=6*10^6 airline dataset in just a few seconds on a laptop.

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