Maximum Likelihood Estimation for Entity Ranking under Iterative Synthetic Data Augmentation

arXiv:2609.32987 · stat.ME, math.ST, stat.ML, stat.TH · Submitted 2026-09-26 · Read on arXiv

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

Submitted: 2026-09-26

Updated: 2026-09-26

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

Terminology

• Data Augmentation — 15× in this paper · explained in 7 episodes
• Loss Function — 6× in this paper · explained in 2 episodes
• Regularization — 6× in this paper · explained in 3 episodes
• Uncertainty Quantification — 6× in this paper · explained in 8 episodes
• Aggregation — 5× in this paper · explained in 1 episode
• Generative Model — 5× in this paper · explained in 2 episodes
• Pairwise Comparisons — 5× in this paper · explained in 2 episodes
• Reinforcement Learning — 5× in this paper · explained in 4 episodes
• Bradley-Terry model — 4× in this paper · explained in 2 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Matrix Completion — 2× in this paper · explained in 3 episodes
• Objective Function — 2× in this paper · explained in 2 episodes
• Rectified Flow — 2× in this paper · explained in 3 episodes
• Sample Complexity — 2× in this paper · explained in 15 episodes
• Theoretical Guarantees — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Non-asymptotic Analysis — 1× in this paper · explained in 2 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes

Sources

• Harnessing Synthetic Data from Generative AI for Statistical Inference
• The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes
• Constitutional AI: Harmlessness from AI Feedback
• Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop
• Perturbation is All You Need for Extrapolating Language Models
• Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
• Universality of the $\pi^2/6$ Pathway in Avoiding Model Collapse
• Covariate Assisted Entity Ranking with Sparse Intrinsic Scores
• Uncertainty Quantification for Ranking with Heterogeneous Preferences
• Recent advances in the Bradley--Terry Model: theory, algorithms, and applications
• Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
• The Llama 3 Herd of Models
• Recursive Learning Without Collapse: A Weighting-Based Stabilization Framework
• Reinforcement Learning from Human Feedback with Active Queries
• Collapse or Thrive? Perils and Promises of Synthetic Data in a Self-Generating World
• Quantifying Error Propagation and Model Collapse in Diffusion Models
• RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
• Efficient Inference for Covariate-adjusted Bradley-Terry Model with Covariate Shift
• Uncertainty Quantification for Large Language Model Reward Learning under Heterogeneous Human Feedback
• Bias-Corrected Data Synthesis for Imbalanced Learning

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