5 papers
From Synthetic Priors to Model Behavior: Structural Coverage in Tabular Foundation Models
He Zhao, Ryan Thompson, Daniel M. Steinberg +3
Tabular foundation models (TFMs) are commonly pretrained on large collections of procedurally generated synthetic tasks, yet it remains unclear how well these synthetic pretraining…
Structure Learning on Clustered Data
Ryan Thompson, Matt P. Wand, Veerabhadran Baladandayuthapani
Recent algorithmic advances have made directed acyclic graph (DAG) structure learning scalable for causal discovery. Yet, the currently available techniques assume a completely hom…
Arrow: A Foundation Model for Causal Discovery
Ryan Thompson, He Zhao, Daniel M. Steinberg +1
We introduce Arrow, a foundation model for zero-shot causal discovery on observational tabular data. Arrow factorizes a directed acyclic graph into an undirected skeleton and a top…
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…
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…