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Models and inference for temporal Gaussian processes

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Reformulating Gaussian process (GP) models as stochastic differential equations (SDE) opens up new opportunities to draw comparisons between classical signal processing algorithms and the state of the art inference methods that dominate the current machine learning literature. In this talk, I’ll discuss the SDE form of the popular “spectral mixture” GP, showing how in the temporal case we can think of such a model as performing probabilistic time-frequency analysis. Following this, I will discuss recent advances in approximate inference for temporal GP models, focusing on power expectation propagation (EP), which is a natural fit for sequential data. Lastly, I will present new work showing the conditions under which power EP recovers traditional methods such as the extended Kalman filter.

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

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