3 papers
stat.ME2026
Hierarchical Partial-Order Models for Ranking
Dongqing Li, Geoff K. Nicholls, Jeong Eun Lee +2
Rank aggregation combines information from ordered lists ranking items by preference. Classical parametric models for such data, including the Mallows and Plackett-Luce models, ass…
stat.ML2026
A Differentiable Bayesian Relaxation for Latent Partial-Order Inference
Dongqing Li, Geoff K. Nicholls, Shiyi Sun +1
Many ranking and agent trace datasets are recorded as linear orders even though their latent structure is only partially ordered. This is especially common in agent and workflow tr…
stat.AP2026
De-Linearizing Agent Traces: Bayesian Inference of Latent Partial Orders for Efficient Execution
Dongqing Li, Zheqiao Cheng, Geoff K. Nicholls +1
AI agents increasingly execute procedural workflows as sequential action traces, which obscures latent concurrency and induces repeated step-by-step reasoning. We introduce BPOP, a…