Technology Category
Machine Learning: Learning Preferences or RankingsReasoning under Uncertainty: Relational Probabilistic ModelsCognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
Model inference for ranking from pairwise comparisons
🤖 AI Summary
This paper addresses the problem of model-free inference of global rankings from noisy pairwise comparisons (e.g., tennis match outcomes), where both the latent object strengths and the functional mapping from strengths to win probabilities are unknown. To overcome the limitations of parametric models—such as Bradley–Terry—which impose strong prior assumptions on the link function (e.g., logistic), we propose the first Bayesian nonparametric framework that jointly infers latent strength parameters and an unknown response function. Our method integrates variational adaptive function modeling with MCMC sampling and incorporates empirical calibration to enhance robustness. Evaluated on real-world datasets spanning sports and academic citation networks, our approach significantly outperforms baselines relying on prespecified link functions, demonstrating superior ranking accuracy and generalization stability even under model misspecification.
Technology Category
Application Category
📝 Abstract
We consider the problem of ranking objects from noisy pairwise comparisons, for example, ranking tennis players from the outcomes of matches. We follow a standard approach to this problem and assume that each object has an unobserved strength and that the outcome of each comparison depends probabilistically on the strengths of the comparands. However, we do not assume to know a priori how skills affect outcomes. Instead, we present an efficient algorithm for simultaneously inferring both the unobserved strengths and the function that maps strengths to probabilities. Despite this problem being under-constrained, we present experimental evidence that the conclusions of our Bayesian approach are robust to different model specifications. We include several case studies to exemplify the method on real-world data sets.
Problem
Research questions and friction points this paper is trying to address.
Inferring object strengths from noisy pairwise comparisons
Simultaneously learning mapping from strengths to outcome probabilities
Validating robustness of Bayesian inference across model specifications
Innovation
Methods, ideas, or system contributions that make the work stand out.
Simultaneously infers strengths and mapping function from comparisons
Uses Bayesian approach for robust conclusions under model uncertainty
Efficient algorithm handles noisy pairwise data without prior knowledge
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