Population-Level Generative Modeling for Ranking Data

arXiv:2608.08422 · stat.ME, cs.LG, math.ST, stat.ML, stat.TH · Submitted 2026-08-11 · Read on arXiv

Zhaoyang Shi

stat.ME, cs.LG, math.ST, stat.ML, stat.TH

Submitted: 2026-08-11

Updated: 2026-08-12

Code: https://github.com/10Mzys/LPSE-FM

Project page: https://preflib.github.io/PrefLib-Jekyll

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Flow Matching — 28× in this paper · explained in 22 episodes
• Generative Modeling — 17× in this paper · explained in 3 episodes
• Generative Model — 10× in this paper · explained in 2 episodes
• Latent Preference Simplex Embedding — 6× in this paper
• Fidelity — 4× in this paper · explained in 2 episodes
• Pairwise Comparisons — 4× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Decoder — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 3 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Latent Variables — 2× in this paper · explained in 2 episodes
• Meta-learning — 2× in this paper · explained in 3 episodes
• Sketched Vertex Search — 2× in this paper
• Successive Projection — 2× in this paper
• Theoretical Guarantees — 2× in this paper · explained in 2 episodes
• Uncertainty Quantification — 2× in this paper · explained in 9 episodes
• Convergence Guarantees — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• ELBO (Evidence Lower Bound) — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Normalizing Flow — 1× in this paper · explained in 4 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Quantifying Uncertainty — 1× in this paper · explained in 5 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Wasserstein Distance — 1× in this paper · explained in 6 episodes

Sources

• Optimal Demixing of Nonparametric Densities
• Action is All You Need: Dual-Flow Generative Ranking Network for Recommendation
• Towards Large-scale Generative Ranking
• Flow Matching for Generative Modeling
• Flow Matching Guide and Code
• Fundamental Limits of Game-Theoretic LLM Alignment: Smith Consistency and Preference Matching
• Optimal Scheduling of Dynamic Transport

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