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Poster
Scalable Training of Inference Networks for Gaussian-Process Models
Jiaxin Shi · Mohammad Emtiyaz Khan · Jun Zhu

Wed Jun 12 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #227
in Posters Wed »

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 mirror-descent algorithm. At each iteration, this only requires considering a finite number of input locations, resulting in a scalable and easy-to-implement algorithm. Empirical results show comparable and, sometimes, superior performance to existing sparse variational GP methods.

Keywords: Approximate Inference · Bayesian Deep Learning · Gaussian Processes

Author Information

Jiaxin Shi (Tsinghua University)
Emti Khan (RIKEN)
Jun Zhu (Tsinghua University)

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