Deep Gaussian Processes on Directed Acyclic Graphs

arXiv:2607.09645 · stat.ML, cs.LG, math.ST, stat.CO, stat.ME · Submitted 2026-07-10 · Read on arXiv

Federico L. Perlino, Oliver Hamelijnck, Adam M. Johansen, Theodoros Damoulas

stat.ML, cs.LG, math.ST, stat.CO, stat.ME

Submitted: 2026-07-10

Comments: 75 pages, 14 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fidelity — 71× in this paper · explained in 2 episodes
• Topological Order — 15× in this paper · explained in 4 episodes
• Composition — 11× in this paper · explained in 2 episodes
• Conditional Independence — 10× in this paper · explained in 2 episodes
• Directed Acyclic Graph — 10× in this paper
• Editor — 9× in this paper · explained in 2 episodes
• Hyperparameters — 6× in this paper · explained in 2 episodes
• Latent Variables — 6× in this paper · explained in 2 episodes
• Adam — 5× in this paper · explained in 3 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Papers — 5× in this paper · explained in 3 episodes
• Causal Inference — 4× in this paper · explained in 5 episodes
• Sparsity — 4× in this paper · explained in 5 episodes
• Degeneracy — 3× in this paper · explained in 4 episodes
• Directed Acyclic Graph (DAG) — 3× in this paper · explained in 2 episodes
• Monotonicity — 3× in this paper · explained in 2 episodes
• Clarity — 2× in this paper · explained in 3 episodes
• Expressivity — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 14 episodes
• Homogeneity — 2× in this paper · explained in 2 episodes
• Hypergraph — 2× in this paper · explained in 2 episodes
• Hypergraphs — 2× in this paper · explained in 2 episodes
• Inductive Bias — 2× in this paper · explained in 3 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Kernel decomposition — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 3 episodes
• Uncertainty Propagation — 2× in this paper · explained in 4 episodes
• Uncertainty Quantification — 2× in this paper · explained in 9 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Admissibility — 1× in this paper · explained in 2 episodes
• Bayesian Inference — 1× in this paper · explained in 6 episodes
• Bayesian Optimization — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 9 episodes
• Causal Discovery — 1× in this paper · explained in 3 episodes
• Computational Efficiency — 1× in this paper · explained in 2 episodes
• Continuous Ranked Probability Score (CRPS) — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Hierarchical Structure — 1× in this paper · explained in 3 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Robustness — 1× in this paper · explained in 6 episodes
• Shapley values — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Deep Gaussian Processes for Multi-fidelity Modeling
• Variational Auto-encoded Deep Gaussian Processes
• Bayesian Causal Inference with Gaussian Process Networks
• Nested Variational Compression in Deep Gaussian Processes
• Sparse Techniques for Regression in Deep Gaussian Processes
• Amortized Variational Inference for Deep Gaussian Processes

Related papers

• Behavior of prediction performance metrics with rare events
• Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
• A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion Priors
• Scalable Posterior Uncertainty for Flexible Density-Based Clustering
• A Transport Geometry of Benign Overfitting
• Efficient quadratic entropy with distance sketches