5 citations · 14 across the 13 of their papers we have counts for
14 papers
Bradley-Terry model under general comparison graphs
Ruijian Han, Yuanhang Luo, Yiming Xu
The Bradley-Terry model is a parametric model for ranking from pairwise comparisons. Existing asymptotic theory for the maximum likelihood estimator (MLE) in regimes where the numb…
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 advances in the Bradley-Terry (BT) model and its extensions. We focus on statistical and computational aspects, with particular emphasis on the regime i…
Statistical ranking with dynamic covariates
Pinjun Dong, Ruijian Han, Binyan Jiang +1
We introduce a general covariate-assisted statistical ranking model within the Plackett--Luce framework. Unlike previous studies focusing on individual effects with fixed covariate…
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…
An approximate control variates approach to multifidelity distribution estimation
Ruijian Han, Boris Kramer, Dongjin Lee +2
Forward simulation-based uncertainty quantification that studies the distribution of quantities of interest (QoI) is a crucial component for computationally robust engineering desi…
Statistical Inference in High-dimensional Generalized Linear Models with Streaming Data
Lan Luo, Ruijian Han, Yuanyuan Lin +1
In this paper we develop an online statistical inference approach for high-dimensional generalized linear models with streaming data for real-time estimation and inference. We prop…
Probabilistic methods for approximate archetypal analysis
Ruijian Han, Braxton Osting, Dong Wang +1
Archetypal analysis is an unsupervised learning method for exploratory data analysis. One major challenge that limits the applicability of archetypal analysis in practice is the in…
Online Debiased Lasso for Streaming Data
Ruijian Han, Lan Luo, Yuanyuan Lin +1
We propose an online debiased lasso (ODL) method for statistical inference in high-dimensional linear models with streaming data. The proposed ODL consists of an efficient computat…
A General Pairwise Comparison Model for Extremely Sparse Networks
Ruijian Han, Yiming Xu, Kani Chen
Statistical inference using pairwise comparison data is an effective approach to analyzing large-scale sparse networks. In this paper, we propose a general framework to model the m…
Curiosity-Driven Recommendation Strategy for Adaptive Learning via Deep Reinforcement Learning
Ruijian Han, Kani Chen, Chunxi Tan
The design of recommendations strategies in the adaptive learning system focuses on utilizing currently available information to provide individual-specific learning instructions f…