1 citations · 1 across the 3 of their papers we have counts for
5 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…
Minimax-Optimal Spectral Clustering with Covariance Projection for High-Dimensional Anisotropic Mixtures
Chengzhu Huang, Yuqi Gu
In mixture models, anisotropic noise within each cluster is widely present in real-world data. This work investigates both computationally efficient procedures and fundamental stat…
High-order Accurate Inference on Manifolds
Chengzhu Huang, Anru R. Zhang
We present a new framework for statistical inference on Riemannian manifolds that achieves high-order accuracy, addressing the challenges posed by non-Euclidean parameter spaces fr…
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…
SITCOM: Step-wise Triple-Consistent Diffusion Sampling for Inverse Problems
Ismail Alkhouri, Shijun Liang, Cheng-Han Huang +4
Diffusion models (DMs) are a class of generative models that allow sampling from a distribution learned over a training set. When applied to solving inverse problems, the reverse s…