81 citations · 86 across the 8 of their papers we have counts for
8 papers
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…
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…
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,…
High-Dimensional Inference for Generalized Linear Models with Hidden Confounding
Jing Ouyang, Kean Ming Tan, Gongjun Xu
Statistical inferences for high-dimensional regression models have been extensively studied for their wide applications ranging from genomics, neuroscience, to economics. However,…
Exponential Family Graphical Models: Correlated Replicates and Unmeasured Confounders, with Applications to fMRI Data
Yanxin Jin, Yang Ning, Kean Ming Tan
Graphical models have been used extensively for modeling brain connectivity networks. However, unmeasured confounders and correlations among measurements are often overlooked durin…
Model Linkage Selection for Cooperative Learning
Jiaying Zhou, Jie Ding, Kean Ming Tan +1
We consider a distributed learning setting where each agent/learner holds a specific parametric model and data source. The goal is to integrate information across a set of learners…
Learning Graphical Models With Hubs
Kean Ming Tan, Palma London, Karthik Mohan +3
We consider the problem of learning a high-dimensional graphical model in which certain hub nodes are highly-connected to many other nodes. Many authors have studied the use of an…
The Cluster Graphical Lasso for improved estimation of Gaussian graphical models
Kean Ming Tan, Daniela Witten, Ali Shojaie
We consider the task of estimating a Gaussian graphical model in the high-dimensional setting. The graphical lasso, which involves maximizing the Gaussian log likelihood subject to…