Population-Level Generative Modeling for Ranking Data
Ranking data arise in scientific and machine learning applications, including recommendation systems, information retrieval, voting, marketing, and AI preference ranking from human feedback. Existing statistical work has primarily focused on inference tasks such as preference estimation, rank aggregation, and ranking prediction. However, generating realistic synthetic rankings from an observed population is important for privacy-preserving data sharing, benchmark construction, simulation, and uncertainty quantification. This task is challenging because rankings are high-dimensional combinatorial objects with non-Euclidean dependence structures, while ranking populations often exhibit subs...
| Authors | Zhaoyang Shi |
|---|---|
| Primary category | stat.ME |
| Published | 9 August 2026 |
| Last updated at source | 11 August 2026 |
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