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. • Deep Generative Modeling for Cognitive Diagnosis via Exploratory DeepCDMs
     Jia Liu, and Yuqi Gu✉. Preprint (2025) [PDF]
2. • Spectral Clustering with Likelihood Refinement is Optimal for Latent Class Recovery
     Zhongyuan Lyu, and Yuqi Gu✉. arXiv preprint arXiv:2506.07167 (2025) [arXiv]
3. • Identifiability of Latent Causal Graphical Models without Pure Children
     Seunghyun Lee, and Yuqi Gu. arXiv preprint arXiv:2505.18410 (2025) [arXiv]
4. • Bayesian Deep Latent Class Regression
     Yuren Zhou, Yuqi Gu, and David B. Dunson. arXiv preprint arXiv:2503.17531 (2025) [arXiv]
5. • Minimax-Optimal Dimension-Reduced Clustering for High-Dimensional Nonspherical Mixtures
     Chengzhu Huang, and Yuqi Gu✉. arXiv preprint arXiv:2502.02580 (2025) [arXiv]
6. • Unfolding Tensors to Identify the Graph in Discrete Latent Bipartite Graphical Models
     Yuqi Gu✉. arXiv preprint arXiv:2501.10897 (2025) [arXiv]
7. • Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers
     Seunghyun Lee, and Yuqi Gu✉. arXiv preprint arXiv:2501.01414 (2025) [arXiv] [Code]
     (This paper received American Statistical Association (ASA) Statistical Learning and Data Science (SLDS) Section 2025 Student Paper Award)
8. • 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]
9. • Adaptive Transfer Clustering: A Unified Framework
     Yuqi Gu⁺, Zhongyuan Lyu⁺, and Kaizheng Wang⁺. arXiv preprint arXiv:2410.21263 (2024) [arXiv] [Code]
10. • 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]
11. • Bayesian Deep Generative Models for Multiplex Networks with Multiscale Overlapping Clusters
      Yuren Zhou, Yuqi Gu, and David B. Dunson. arXiv preprint arXiv:2405.20936 (2024) [arXiv]
      (This paper received ENAR 2025 Distinguished Student Paper Award)

Publications

1. • Degree-heterogeneous Latent Class Analysis for High-dimensional Discrete Data
     Zhongyuan Lyu, Ling Chen, and Yuqi Gu✉
     Journal of the American Statistical Association (2025), accepted. [arXiv] [Journal] [Code]
2. • Exploratory General-response Cognitive Diagnostic Models with Higher-order Structures
     Jia Liu, Seunghyun Lee, and Yuqi Gu✉
     Psychometrika (2025), accepted. [Journal] [PDF] [Code]
3. • Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness
     Ye Tian, Yuqi Gu, and Yang Feng
     Journal of Machine Learning Research (2025), accepted. [arXiv] [Code]
     (This paper received ASA SLDS Section 2025 Student Paper Award)
4. • Blessing of Dependence: Identifiability and Geometry of Discrete Models with Multiple Binary Latent Variables
     Yuqi Gu✉
     Bernoulli (2025), 31 (2): 948–972. [arXiv] [Journal]
5. • Going Deep in Diagnostic Modeling: Deep Cognitive Diagnostic Models (DeepCDMs)
     Yuqi Gu✉
     Psychometrika (2024), 89: 118–150. [Journal] [PDF]
6. • 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]
     (This paper received 2024 Institute of Mathematical Statistics (IMS) Hannan Graduate Student Travel Award)
7. • 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]
8. • 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]
     (This paper received 2023 Psychometric Society Student Travel Award)
9. • 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]
10. • 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]
11. • 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]
12. • 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]
13. • Generic Identifiability of the DINA Model and Blessing of Latent Dependence
      Yuqi Gu✉
      Psychometrika (2023), 88: 117–131. [Journal] [PDF]
14. • 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]
15. • 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]
16. • Identifiability of Hierarchical Latent Attribute Models
      Yuqi Gu, and Gongjun Xu
      Statistica Sinica (2023), 33: 2561–2591. [arXiv] [Journal]
17. • Sufficient and Necessary Conditions for the Identifiability of the Q-matrix
      Yuqi Gu, and Gongjun Xu
      Statistica Sinica (2021), 31: 449–472. [arXiv] [Journal]
18. • Partial Identifiability of Restricted Latent Class Models
      Yuqi Gu, and Gongjun Xu
      Annals of Statistics (2020), 48 (4): 2082–2107. [arXiv] [Journal]
19. • 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]
20. • 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]
21. • Hypothesis Testing of the Q-matrix
      Yuqi Gu, Jingchen Liu, Gongjun Xu, and Zhiliang Ying
      Psychometrika (2018), 83 (3): 515–537. [Journal]