DAG-DGPs place GP priors on functions along a DAG, prove non-collapse bounds under separating nodes and intermediate observations, and give structured VI that retains compositional uncertainty and explaining-away, with SOTA multi-fidelity results.
Nested Variational Compression in Deep Gaussian Processes
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abstract
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.
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Deep Gaussian Processes on Directed Acyclic Graphs
DAG-DGPs place GP priors on functions along a DAG, prove non-collapse bounds under separating nodes and intermediate observations, and give structured VI that retains compositional uncertainty and explaining-away, with SOTA multi-fidelity results.