Research

Underlined are student or postdoc authors under my supervision. ✉ indicates I am the corresponding author.
* indicates co-first authors. ⁺ indicates alphabetical authorship.

Preprints

1. • Generalized Grade-of-Membership Estimation for High-dimensional Locally Dependent Data
     Ling Chen*, Chengzhu Huang*, and Yuqi Gu✉
     arXiv preprint arXiv:2412.19796 (2024)
     [arXiv] [Code]
2. • Exploratory General-response Cognitive Diagnostic Models with Higher-order Structures
     Jia Liu, Seunghyun Lee, and Yuqi Gu✉
     Preprint. (2024)
     [PDF]
3. • Adaptive Transfer Clustering: A Unified Framework
     Yuqi Gu⁺, Zhongyuan Lyu⁺, and Kaizheng Wang⁺
     arXiv preprint arXiv:2410.21263 (2024)
     [arXiv] [Code]
4. • A Blockwise Mixed Membership Model for Multivariate Longitudinal Data: Discovering Clinical Heterogeneity and Identifying Parkinson’s Disease Subtypes
     Kai Kang✉, and Yuqi Gu✉
     arXiv preprint arXiv:2410.01235 (2024)
     [arXiv]
5. • Degree-heterogeneous Latent Class Analysis for High-dimensional Discrete Data
     Zhongyuan Lyu, Ling Chen, and Yuqi Gu✉
     arXiv preprint arXiv:2402.18745 (2024)
     [arXiv] [Code]
6. • Bayesian Deep Generative Models for Replicated Networks with Multiscale Overlapping Clusters
     Yuren Zhou, Yuqi Gu, and David B. Dunson
     arXiv preprint arXiv:2405.20936 (2024)
     [arXiv]
7. • Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness
     Ye Tian, Yuqi Gu, and Yang Feng
     arXiv preprint arXiv:2303.17765 (2023)
     [arXiv]

Publications

1. • Blessing of Dependence: Identifiability and Geometry of Discrete Models with Multiple Binary Latent Variables
     Yuqi Gu✉
     Bernoulli (2024), accepted.
     [arXiv] [Journal]
2. • Going Deep in Diagnostic Modeling: Deep Cognitive Diagnostic Models (DeepCDMs)
     Yuqi Gu✉
     Psychometrika (2024), 89: 118–150.
     [Journal] [PDF]
3. • Latent Conjunctive Bayesian Network: Unify Attribute Hierarchy and Bayesian Network for Cognitive Diagnosis
     Seunghyun Lee, and Yuqi Gu✉
     Annals of Applied Statistics (2024), 18 (3): 1988–2011.
     [arXiv] [Journal] [Code]
4. • A Spectral Method for Identifiable Grade of Membership Analysis with Binary Responses
     Ling Chen, and Yuqi Gu✉
     Psychometrika (2024), 89: 626–657.
     [arXiv] [Journal] [Code]
5. • New Paradigm of Identifiable General-response Cognitive Diagnostic Models: Beyond Categorical Data
     Seunghyun Lee, and Yuqi Gu✉
     Psychometrika (2024), 89: 1304–1336.
     [Journal] [PDF] [Code]
6. • New directions in algebraic statistics: Three challenges from 2023
     Yulia Alexandr, Miles Bakenhus, Mark Curiel, Sameer K. Deshpande, Elizabeth Gross, Yuqi Gu, Max Hill, Joseph Johnson, Bryson Kagy, Vishesh Karwa, Jiayi Li, Hanbaek Lyu, Sonja Petrovic, and Jose Israel Rodriguez
     Algebraic Statistics (2024), 15 (2): 357–382.
     [arXiv] [Journal]
7. • Bayesian Pyramids: Identifiable Multilayer Discrete Latent Structure Models for Discrete Data
     Yuqi Gu✉, and David B. Dunson
     Journal of the Royal Statistical Society Series B: Statistical Methodology (2023), 85 (2): 399–426.
     [arXiv] [Journal] [Code]
8. • A Joint MLE Approach to Large-Scale Structured Latent Attribute Analysis
     Yuqi Gu✉, and Gongjun Xu
     Journal of the American Statistical Association (2023), 118 (541): 746–760.
     [arXiv] [Journal]
9. • Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data
     Yuqi Gu✉, Elena A. Erosheva, Gongjun Xu, and David B. Dunson
     Journal of Machine Learning Research (2023), 24 (88): 1–49.
     [arXiv] [Journal]
10. • Generic Identifiability of the DINA Model and Blessing of Latent Dependence
      Yuqi Gu✉
      Psychometrika (2023), 88: 117–131.
      [Journal] [PDF]
11. • A Tensor-EM Method for Large-Scale Latent Class Analysis with Binary Responses
      Zhenghao Zeng, Yuqi Gu, and Gongjun Xu
      Psychometrika (2023), 88 (2): 580–612.
      [arXiv] [Journal]
12. • Discussion of “Vintage Factor Analysis with Varimax Performs Statistical Inference” by Rohe and Zeng
      Yinqiu He, Yuqi Gu, and Zhiliang Ying
      Journal of the Royal Statistical Society Series B: Statistical Methodology (2023), 85 (4): 1071–1074.
      [Journal]
13. • Identifiability of Hierarchical Latent Attribute Models
      Yuqi Gu, and Gongjun Xu
      Statistica Sinica (2023), 33: 2561–2591.
      [arXiv] [Journal]
14. • Sufficient and Necessary Conditions for the Identifiability of the Q-matrix
      Yuqi Gu, and Gongjun Xu
      Statistica Sinica (2021), 31: 449–472.
      [arXiv] [Journal]
15. • Partial Identifiability of Restricted Latent Class Models
      Yuqi Gu, and Gongjun Xu
      Annals of Statistics (2020), 48 (4): 2082–2107.
      [arXiv] [Journal]
16. • Learning Attribute Patterns in High-dimensional Structured Latent Attribute Models
      Yuqi Gu, and Gongjun Xu
      Journal of Machine Learning Research (2019), 20 (1): 1–58.
      [arXiv] [Journal]
17. • The Sufficient and Necessary Condition for the Identifiability and Estimability of the DINA Model
      Yuqi Gu, and Gongjun Xu
      Psychometrika (2019), 84 (2): 468–483.
      [arXiv] [Journal]
18. • Hypothesis Testing of the Q-matrix
      Yuqi Gu, Jingchen Liu, Gongjun Xu, and Zhiliang Ying
      Psychometrika (2018), 83 (3): 515–537.
      [Journal]