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Deep Gaussian Processes on Directed Acyclic Graphs

paperactiveprovisionaldeep-gaussian-processes-on-directed-acyclic-graphs-f1b6c478·1 events·first seen Jul 13, 2026

Aliases: Deep Gaussian Processes on Directed Acyclic Graphs

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4arXiv · cs.LG·Jul 13, 2026·source ↗

Deep Gaussian Processes on Directed Acyclic Graphs: theory and structured variational inference

A new arXiv preprint introduces Deep Gaussian Processes (DGPs) defined over directed acyclic graphs (DAGs), providing a principled probabilistic framework for compositional function estimation in causal models, multi-fidelity simulations, and gene-regulatory networks. The authors derive theoretical results on prior-collapse behavior and information preservation as a function of graph topology, and propose a structured variational approximation that respects graph dependencies and collider explaining-away effects. Empirical validation covers a protein signalling network and a multi-fidelity heavy-ion collision emulation task, claiming state-of-the-art performance. The work is a methodological contribution at the intersection of probabilistic ML and structured graphical models.

Evaluation and Benchmarking Deep Gaussian Processes on Directed Acyclic Graphs