Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
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…
cs.LG2026
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…
cs.LG2026
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…