Structure Learning on Clustered Data

arXiv:2607.08238 · cs.LG, stat.ME, stat.ML · Submitted 2026-07-09 · Read on arXiv

Ryan Thompson, Matt P. Wand, Veerabhadran Baladandayuthapani

cs.LG, stat.ME, stat.ML

Submitted: 2026-07-09

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

Terminology

• Sparsity — 22× in this paper · explained in 5 episodes
• Consistency — 13× in this paper · explained in 3 episodes
• F1 Score — 10× in this paper · explained in 4 episodes
• Hamming Distance — 9× in this paper · explained in 2 episodes
• Causal Discovery — 6× in this paper · explained in 3 episodes
• Objective Function — 6× in this paper · explained in 2 episodes
• False Negatives — 5× in this paper · explained in 2 episodes
• Lasso — 3× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Adam — 2× in this paper · explained in 3 episodes
• Causal Inference — 2× in this paper · explained in 5 episodes
• Directed Acyclic Graph (DAG) — 2× in this paper · explained in 2 episodes
• Kullback–Leibler Divergence — 2× in this paper · explained in 2 episodes
• Absorb — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Combinatorial Optimization — 1× in this paper · explained in 2 episodes
• Conditional Independence — 1× in this paper · explained in 2 episodes
• Copula — 1× in this paper · explained in 2 episodes
• Dimensionality Reduction — 1× in this paper · explained in 2 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 6 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data
• Scalable Subset Selection in Linear Mixed Models

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