7 papers
Convergence analysis of a family of Zermelo-type iterations for the Bradley--Terry model
Ruijian Han, Ding Lu, Yiming Xu
Zermelo's algorithm is a classical method for computing the maximum likelihood estimator in the Bradley--Terry (BT) model, but its convergence can be slow in practice. To accelerat…
Variance-aware Reward Modeling with Anchor Guidance
Shuxing Fang, Ruijian Han, Liangyu Zhang +1
Standard Bradley--Terry (BT) reward models are limited when human preferences are pluralistic. Although soft preference labels preserve disagreement information, BT can only expres…
Deep Ranking with Heterogeneous Effects
Yuanhang Luo, Shuxing Fang, Ruijian Han +1
Classical latent-score ranking models often fail to distinguish objects' intrinsic scores from contextual effects, which are typically nonlinear and can dominate the observed outco…
Recent advances in the Bradley--Terry Model: theory, algorithms, and applications
Shuxing Fang, Ruijian Han, Yuanhang Luo +1
This article surveys recent progress in the Bradley-Terry (BT) model and its extensions. We focus on the statistical and computational aspects, with emphasis on the regime in which…
Statistical inference for pairwise comparison models
Ruijian Han, Wenlu Tang, Yiming Xu
Pairwise comparison models have been widely used for utility evaluation and rank aggregation across various fields. The increasing scale of modern problems underscores the need to…
A unified analysis of likelihood-based estimators in the Plackett--Luce model
Ruijian Han, Yiming Xu
The Plackett--Luce model has been extensively used for rank aggregation in social choice theory. A central statistical question in this model concerns estimating the utility vector…