Nested Variational Compression in Deep Gaussian Processes
This work addresses a computational bottleneck for researchers and practitioners using deep Gaussian processes in probabilistic modeling, representing an incremental improvement over existing methods.
The paper tackles the problem of tractable inference in deep Gaussian processes by introducing nested variational compression, which extends previous variational compression methods to allow for parallelization and adaptation to stochastic variational inference.
Deep Gaussian processes provide a flexible approach to probabilistic modelling of data using either supervised or unsupervised learning. For tractable inference approximations to the marginal likelihood of the model must be made. The original approach to approximate inference in these models used variational compression to allow for approximate variational marginalization of the hidden variables leading to a lower bound on the marginal likelihood of the model [Damianou and Lawrence, 2013]. In this paper we extend this idea with a nested variational compression. The resulting lower bound on the likelihood can be easily parallelized or adapted for stochastic variational inference.