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.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…
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…