Covariate Assisted Entity Ranking with Sparse Intrinsic Scores
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Abstract (arXiv)
This paper addresses the item ranking problem with associate covariates, focusing on scenarios where the preference scores can not be fully explained by covariates, and the remaining intrinsic scores, are sparse. Specifically, we extend the pioneering Bradley-Terry-Luce (BTL) model by incorporating covariate information and considering sparse individual intrinsic scores. Our work introduces novel model identification conditions and examines the regularized penalized Maximum Likelihood Estimator (MLE) statistical rates. We then construct a debiased estimator for the penalized MLE and analyze its distributional properties. Additionally, we apply our method to the goodness-of-fit test for models with no latent intrinsic scores, namely, the covariates fully explaining the preference scores of individual items. We also offer confidence intervals for ranks. Our numerical studies lend further support to our theoretical findings, demonstrating validation for our proposed method
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 6 of its 42 references that are in the map. First posted on arXiv 9 Jul 2024.
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6 of 42 references.
References · 6
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2015An Introduction to Matrix Concentration Inequalitiescited by 391
- 2022Uncertainty Quantification of MLE for Entity Ranking with Covariatescited by 16
- 2023Spectral Ranking Inferences based on General Multiway Comparisonscited by 10
- 2023A unified analysis of likelihood-based estimators in the Plackett--Luce modelcited by 8
- 2020Learning Mixtures of Random Utility Models with Features from Incomplete Preferencescited by 4
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Citing papers per year: 2024: 2; 2025: 2; 2026: 2.
6 papers.
- 2024Dual Active Learning for Reinforcement Learning from Human Feedbackcited by 19
- 2025Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuningcited by 13
- 2024Low-Rank Contextual Reinforcement Learning from Heterogeneous Human Feedbackcited by 10
- 2025Uncertainty Quantification for Large Language Model Reward Learning under Heterogeneous Human Feedbackcited by 7
- 2026A Judge-Aware Ranking Framework for Evaluating Large Language Models without Ground Truthcited by 7
- 2026Reinforcement Learning from Human Feedback: A Statistical Perspectivecited by 4
- 2026Reinforcement Learning from Human Feedback: A Statistical Perspective2 Apr 2026
- 2026A Judge-Aware Ranking Framework for Evaluating Large Language Models without Ground Truth29 Jan 2026
- 2025Uncertainty Quantification for Large Language Model Reward Learning under Heterogeneous Human Feedback2 Dec 2025
- 2025Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning3 Apr 2025
- 2024Low-Rank Contextual Reinforcement Learning from Heterogeneous Human Feedback27 Dec 2024
- 2024Dual Active Learning for Reinforcement Learning from Human Feedback3 Oct 2024
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- 2021Lagrangian Inference for Ranking Problems
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- 2026Nonparametric LLM Evaluation from Preference Data
- 2018Ranking with Features: Algorithm and A Graph Theoretic Analysis ↗
- 2026Population-Level Generative Modeling for Ranking Data