AI RESEARCH
Heterogeneous Ordinal Structure Learning with Bayesian Nonparametric Complexity Discovery
arXiv CS.LG
•
ArXi:2605.04191v1 Announce Type: cross Public attitudes toward artificial intelligence are heterogeneous, ordinally measured, and poorly captured by any single dependency graph. Existing ordinal structure learners assume a shared directed acyclic graph (DAG) across all respondents; recent heterogeneous ordinal graphical-model approaches focus on subgroup discovery rather than confirmatory cluster-specific DAG estimation; and latent profile analyses discard dependency structure entirely. We.