Deep Gaussian Processes on Directed Acyclic Graphs
Many real-world processes can be represented as compositions of functions along a directed acyclic graph (DAG). In causal modelling, these correspond to the underlying mechanisms; in engineering, to multiple fidelity levels; and in gene-regulatory networks, to transcription factors. These functions are partially observed across the DAG, with noisy and heterogeneously sampled measurements, posing significant challenges for reconstruction, uncertainty propagation, and inference. To tackle these challenges, we place priors over functions and naturally arrive at Deep Gaussian Processes over DAGs.
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via arxiv author · 85%Federico L. Perlino →
“Deep Gaussian Processes on Directed Acyclic Graphs”
- LinkedLinked via arxiv author · 85%Oliver Hamelijnck →
“Deep Gaussian Processes on Directed Acyclic Graphs”
- LinkedLinked via arxiv author · 85%Adam M. Johansen →
“Deep Gaussian Processes on Directed Acyclic Graphs”
- LinkedLinked via arxiv author · 85%Theodoros Damoulas →
“Deep Gaussian Processes on Directed Acyclic Graphs”