Scalable Training of Inference Networks for GaussianProcess Models
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Proceedings of the 36th International Conference on Machine Learning, PMLR 97:57585768, 2019.
Abstract
Inference in Gaussian process (GP) models is computationally challenging for large data, and often difficult to approximate with a small number of inducing points. We explore an alternative approximation that employs stochastic inference networks for a flexible inference. Unfortunately, for such networks, minibatch training is difficult to be able to learn meaningful correlations over function outputs for a large dataset. We propose an algorithm that enables such training by tracking a stochastic, functional mirrordescent algorithm. At each iteration, this only requires considering a finite number of input locations, resulting in a scalable and easytoimplement algorithm. Empirical results show comparable and, sometimes, superior performance to existing sparse variational GP methods.
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