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Marginalised Gaussian Processes with Nested Sampling
Fergus Simpson · Vidhi Lalchand · Carl Edward Rasmussen

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @ None #None

Gaussian Process models are a rich distribution over functions with inductive biases controlled by a kernel function. Learning occurs through optimisation of the kernel hyperparameters using the marginal likelihood as the objective. This work proposes nested sampling as a means of marginalising kernel hyperparameters, because it is a technique that is well-suited to exploring complex, multi-modal distributions. We benchmark against Hamiltonian Monte Carlo on time-series and two-dimensional regression tasks, finding that a principled approach to quantifying hyperparameter uncertainty substantially improves the quality of prediction intervals.

Author Information

Fergus Simpson (Secondmind)
Vidhi Lalchand (University of Cambridge)

Ph.D student in Machine learning at Cambridge, I work on Bayesian Non-parametrics, Gaussian Processes, Kernel Learning. Application Areas: High Energy Physics, Astronomy, Science!

Carl Edward Rasmussen (University of Cambridge)

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