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stat.ML2025
Scalable Subset Selection in Linear Mixed Models
Ryan Thompson, Matt P. Wand, Joanna J. J. Wang
Linear mixed models (LMMs), which incorporate fixed and random effects, are key tools for analyzing heterogeneous data, such as in personalized medicine. Nowadays, this type of dat…
stat.ML2024
ProDAG: Projected Variational Inference for Directed Acyclic Graphs
Ryan Thompson, Edwin V. Bonilla, Robert Kohn
Directed acyclic graph (DAG) learning is a central task in structure discovery and causal inference. Although the field has witnessed remarkable advances over the past few years, i…
stat.ML2023
Contextual Directed Acyclic Graphs
Ryan Thompson, Edwin V. Bonilla, Robert Kohn
Estimating the structure of directed acyclic graphs (DAGs) from observational data remains a significant challenge in machine learning. Most research in this area concentrates on l…