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Scalable Gaussian Processes for Scientific Discovery

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Large datasets provide unprecedented opportunities to automatically discover rich statistical structure, from which we can derive new scientific discoveries. Gaussian processes are flexible distributions over functions, which can learn interpretable structure through covariance kernels. In this talk, I introduce an O(N) Gaussian process framework which is capable of learning expressive kernel functions on large datasets. This framework generalizes and provides alternative derivations for classical inducing point methods, and allows one to exploit kernel structure for significant further gains in scalability and accuracy, without requiring severe assumptions. I evaluate this approach for kernel matrix reconstruction, kernel learning, time series modeling, image inpainting, and long range forecasting in spatiotemporal statistics.


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

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