6 citations · 7 across the 11 of their papers we have counts for
12 papers
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…
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…
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…
A Latent Variable Framework for Scaling Laws in Large Language Models
Peiyao Cai, Chengyu Cui, Felipe Maia Polo +6
We propose a statistical framework built on latent variable modeling for scaling laws of large language models (LLMs). Our work is motivated by the rapid emergence of numerous new…
Identifiability and Inference for Generalized Latent Factor Models
Chengyu Cui, Gongjun Xu
Generalized latent factor analysis not only provides a useful latent embedding approach in statistics and machine learning, but also serves as a widely used tool across various sci…
Statistical Inference for Covariate-Adjusted and Interpretable Generalized Factor Model with Application to Testing Fairness
Jing Ouyang, Chengyu Cui, Kean Ming Tan +1
Latent variable models are popularly used to measure latent factors (e.g., abilities and personalities) from large-scale assessment data. Beyond understanding these latent factors,…
Variational Estimation for Multidimensional Generalized Partial Credit Model
Chengyu Cui, Chun Wang, Gongjun Xu
Multidimensional item response theory (MIRT) models have generated increasing interest in the psychometrics literature. Efficient approaches for estimating MIRT models with dichoto…
Consistency Theory of General Nonparametric Classification Methods in Cognitive Diagnosis
Chengyu Cui, Yanlong Liu, Gongjun Xu
Cognitive diagnosis models have been popularly used in fields such as education, psychology, and social sciences. While parametric likelihood estimation is a prevailing method for…
Statistical Inference on Latent Space Models for Network Data
Jinming Li, Shihao Wu, Chengyu Cui +2
Latent space models are powerful statistical tools for modeling and understanding network data. While the importance of accounting for uncertainty in network analysis has been well…