6 papers
From Good Starts to Optimal Inference: Generalized Latent Factor Models with Missingness and Implicit Regularization
Chengzhu Huang, Yuqi Gu
Generalized latent factor models provide a flexible framework for analyzing high-dimensional non-Gaussian data, but principled estimation and uncertainty quantification under missi…
Mixed Membership Model of Low-rank Matrices with Multimodal Extension
David Snider, Zhongyuan Lyu, Jian Kang +1
Matrix-valued observations arise in multiplex networks, neuroimaging, and other domains where population-level patterns are often low-rank and subjects may express several latent p…
Identifiable Bayesian Deep Generative Copulas with Unknown Layer Widths for Data with Arbitrary Marginal Distributions
Joseph Feldman, Yuqi Gu
Deep generative models offer powerful tools for multivariate data analysis, but their black-box architectures are often unidentified and difficult to interpret. We introduce the De…
Generalized Grade-of-Membership Estimation for High-dimensional Locally Dependent Data
Ling Chen, Chengzhu Huang, Yuqi Gu
This work focuses on the mixed membership models for multivariate categorical data widely used for analyzing survey responses and population genetics data. These grade of membershi…
Adaptive Transfer Clustering: A Unified Framework
Yuqi Gu, Zhongyuan Lyu, Kaizheng Wang
We propose a general transfer learning framework for clustering given a main dataset and an auxiliary one about the same subjects. The two datasets may reflect similar but differen…
Degree-heterogeneous Latent Class Analysis for High-dimensional Discrete Data
Zhongyuan Lyu, Ling Chen, Yuqi Gu
The latent class model is a widely used mixture model for multivariate discrete data. Besides the existence of qualitatively heterogeneous latent classes, real data often exhibit a…