89 citations · 455 across the 65 of their papers we have counts for
15 papers · 1 filter
Noisy Matrix Completion under Informative Missingness
Xuanyu Chen, Xiaoyue Niu, Gongjun Xu +1
Noisy matrix completion is a fundamental problem in statistical learning and has attracted a substantial amount of interest over the last two decades. In a variety of applications,…
Hypergraph Representation Learning with Hyperlink Random Effects
Zimeng Li, Shihao Wu, Gongjun Xu +1
Hypergraphs record multi-way interactions among entities. Extracting information from the combinatorial structure underlying observed multi-way interactions is a central task in ma…
Characterizing Identifiability in Boolean Factor Models
Mengqi Lin, Gongjun Xu
Boolean factor models, including prominent subfamilies such as Boolean matrix decompositions and cognitive diagnosis models, find broad applications ranging from social sciences to…
Marginal maximum likelihood estimation and asymptotic theory for latent variable models in high dimensions
Chengyu Cui, Gongjun Xu
This work addresses a longstanding gap in the statistical foundations of marginal maximum likelihood estimation for high-dimensional latent variable models. Marginal maximum likeli…
Genetic association testing with multivariate survival phenotypes under interval censoring
Juhee Lee, Kun Xia, Jianrui Zhang +3
Set-based genetic association tests provide a powerful framework for detecting genetic effects on complex traits by jointly analyzing multiple genetic variants. Although set-based…
Conditional-Independence-Regularized Distributional Autoencoders for Mixed-Type Data
Siyuan Tang, Gongjun Xu, Ji Zhu
Mixed-type data containing both numerical and categorical variables arise in many scientific and real-world applications. Existing representation learning and generative modeling a…
Statistical analysis of block structured latent variable models
Chengyu Cui, Gongjun Xu
This paper studies block structured latent variable models, in which observed variables are grouped into distinct blocks based on their relationships with the underlying latent var…
Stochastic Sensitivity Analysis for Matched Observational Studies
Mengqi Lin, Colin B. Fogarty, Gongjun Xu
Sensitivity analysis asks how strong unmeasured confounding needs to be to explain away an observational study's conclusion. The conventional approach in matched studies conducts i…
Theoretical Analysis of Engression and Reverse Markov Engression
Jiaqi Huang, Gongjun Xu, Ji Zhu
Engression is a recently proposed and effective framework for conditional distribution learning. Its multi-step Reverse Markov extension further improves generative flexibility by…
Efficient Synthetic Network Generation via Latent Embedding Reconstruction
Feifan Jiang, Yinan Bu, Shihao Wu +2
Network data are ubiquitous across the social sciences, biology, and information systems. Generating realistic synthetic network data has broad applications from network simulation…
Multidimensional Item Response Theory under General Latent Distributions
Chengyu Cui, Taoyi Chen, Chun Wang +1
Multidimensional item response theory (MIRT) provides an important psychometric framework for modeling how multiple latent traits jointly influence observed item responses. In most…
Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation
Chengyu Cui, Gongjun Xu
Nonconvex methods have emerged as a dominant approach for low-rank matrix estimation, a problem that arises widely in machine learning and AI for learning and representing high-dim…
Inference on Generalized Latent Variable Models with High-Dimensional Responses and Covariates
Jing Ouyang, Chengyu Cui, Yunxiao Chen +2
Regression models with both high-dimensional responses and covariates have attracted growing attention. Standard multivariate regression models become inadequate when the response…
Beyond Vintage Rotation: Bias-Free Sparse Representation Learning with Oracle Inference
Chengyu Cui, Yunxiao Chen, Jing Ouyang +1
Learning low-dimensional latent representations is a central topic in statistics and machine learning, and rotation methods have long been used to obtain sparse and interpretable r…
Unfolding and Fusion: Debiased Inference for Generalized Multilayer Latent Space Models
Zhaozhe Liu, Gongjun Xu, Haoran Zhang
Multilayer networks have become increasingly ubiquitous across diverse scientific fields, yet their inferential theory remains underdeveloped. We propose a flexible latent space mo…