10 papers
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…
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…
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…
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…
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…