Spectral Sparsification of Laplacian-Constrained Gaussian and H"usler-Reiss Graphical Models

arXiv:2606.16681 · stat.ME, stat.ML · Submitted 2026-06-15 · Read on arXiv

Ignacio Echave-Sustaeta Rodríguez, Aida Abiad, Frank Röttger

stat.ME, stat.ML

Submitted: 2026-06-15

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

Terminology

• Sparsity — 4× in this paper · explained in 5 episodes
• Trace — 4× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Conditional Independence — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Lasso — 2× in this paper · explained in 2 episodes
• Monotonicity — 2× in this paper · explained in 2 episodes
• Annual Symposium on Foundations of Computer Science — 1× in this paper
• F1 Score — 1× in this paper · explained in 4 episodes
• Matrix Completion — 1× in this paper · explained in 4 episodes
• Papers — 1× in this paper · explained in 3 episodes
• Spectral Gap — 1× in this paper · explained in 6 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes

Sources

• Extremal conditional independence for H\"usler-Reiss distributions via modular functions
• Learning extremal graphical structures in high dimensions

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