Keywords: Gaussian processes, sparse variational inference, natural gradients
TL;DR: Leveraging dual-parameterization for efficient inference and learning of hyperparameters in sparse variational GP models
Abstract: Sparse variational Gaussian process (SVGP) methods are a common choice for non-conjugate Gaussian process inference because of their computational benefits. In this paper, we improve their computational efficiency by using a dual parameterization where each data example is assigned dual parameters, similarly to site parameters used in expectation propagation. Our dual parameterization speeds-up inference using natural gradient descent, and provides a tighter evidence lower bound for hyperparameter learning. The approach has the same memory cost as the current SVGP methods, but it is faster and more accurate.
Supplementary Material: pdf
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