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.
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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.