Selected Preprints (see also my Google Scholar):
First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint
Z. Liu, K. Lee, Y. Zhang, and W. Tang. (2026) [arXiv]
Representation Learning with Blockwise Missingness and Signal Heterogeneity
Z. Liu, Y. Tian, and W. Tang. (2026) [arXiv]
(An earlier version of this paper received ASA SLDS Section 2026 Student Paper Award.)
Knowledge-Embedded Latent Projection for Robust Representation Learning
W. Tang, M. Yuan, Z. Xia, and T. Cai. (2026) [arXiv]
Balanced Stochastic Block Model for Community Detection in Signed Networks
Y. Chen*, W. Tang*, and J. Zhu. (2026) [arXiv]
Recurrent Event Analysis with Ordinary Differential Equations
B. Meng, W. Tang, G. Xu, and J. Zhu. (2025) [arXiv]
Minimax Regret Learning for Data with Heterogeneous Subgroups.
W. Mo*, W. Tang*, S. Xue, Y. Liu, and J. Zhu. (2024) [arXiv]
Priority-aware Shapley Value
K. Lee, Z. Liu, W. Tang, and Y. Zhang.
The 43nd International Conference on Machine Learning (2026). (ICML'26). [arXiv]
Nonparametric Inference for Balance in Signed Networks.
X. Chen*, Y. Wang*, and W. Tang.
Biometrika (2026+). Accepted [arXiv][Code]
Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution
Y. Tu*, Z. Liu*, J. Ma, and W. Tang.
Transactions on Machine Learning Research (2026). Accepted [arXiv]
Mini-batch Estimation for Deep Cox Models: Theoretical Foundations and Practical Guidance.
L. Zeng, W. Tang, Z. Ren, and Y. Ding.
Journal of the American Statistical Association: Theory and Methods (JASA) (Special Issue on Statistical Science in AI) (2026+). Accepted [arXiv]
Incorporating External Risk Information with the Cox Model under Population Heterogeneity: Applications to Trans-Ancestry Polygenic Hazard Scores
D. Wang, W. Ye, J. Zhu, G. Xu, W. Tang, M. Zawistowski, L.G. Fritsche, and K. He.
Journal of the Royal Statistical Society: Series A (JRSS-A) (2026+). Accepted [arXiv]
Faithful Group Shapley Value
K. Lee*, Z. Liu*, W. Tang, and Y. Zhang
The 39th Annual Conference on Neural Information Processing Systems (2025). (NeurIPS'25). [arXiv][Code]
(An earlier version of this paper received honorable mention for best paper at the ICML 2025 Workshop DataWorld.)
GraSS: Scalable Influence Function with Sparse Gradient Compression
P. Hu, J. Melkonian, W. Tang, H. Zhao, and J. Ma
The 39th Annual Conference on Neural Information Processing Systems (2025). (NeurIPS'25). [arXiv]
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss
J. Deng, W. Tang, and J. Ma
The 42nd International Conference on Machine Learning (2025). (ICML'25). [arXiv]
Application of Supervised and Semi-Supervised Learning Prediction Models to Predict Progression to Cirrhosis in Chronic Hepatitis C.
Y. Hu*, W. Tang*, L.A. Beste, G.L. Su, G.N. Ioannou, T. Van, J. Zhu, and A.K. Waljee
Artificial Intelligence in Health (2025), 2(2), 87-99. [Journal]
Population-level Balance in Signed Networks.
W. Tang and J. Zhu
Journal of the American Statistical Association: Theory and Methods (JASA) (2024), 120 (550), 751-763. [Journal][arXiv][Code]
(An earlier version of this paper received ASA SLDS Section 2021 Student Paper Award.)
Machine Learning Methodologies to Predict Disease Progression in Chronic Hepatitis B in Africa.
H. Desalegn, X. Yang, Y. Yen, N. Berhe, B. Kenney, G.H. Siwo, W. Tang, J. Zhu, A. Johannessen, and A.K. Waljee
Hepatology Communications (2024), 8(12):e0584. [Journal]
Survival Analysis via Ordinary Differential Equations.
W. Tang, K. He, G. Xu, and J. Zhu
Journal of the American Statistical Association: Theory and Methods (JASA) (2023), 118(544), 2406-2421. [Journal][arXiv][Code]
(An earlier version of this paper received ASA Nonparametric Statistics Section 2020 Student Paper Award and ENAR 2021 Distinguished Student Paper Award.)
Greater Geographic Sharing and Heart Transplantation Waitlist Outcomes Following the 2018 Heart Allocation Policy.
J. Golbus, J. Li, T. Cascino, W. Tang, J. Zhu, M. Colvin, M. Walsh, and B. Nallamothu
Journal of Heart and Lung Transplantation (2023), 42(7), 936-942. [Journal]
SODEN: A Scalable Continuous-time Survival Model through Ordinary Differential Equation Networks.
W. Tang*, J. Ma*, Q. Mei, and J. Zhu
Journal of Machine Learning Research (JMLR) (2022), 23(34):1-29. [Journal] [arXiv] [Code]
Learning-to-rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model.
J. Ma, X. Yi, W. Tang, Z. Zhao, L. Hong, E.H. Chi, and Q. Mei
The 24th International Conference on Artificial Intelligence and Statistics (2021). (AISTATS'21). [arXiv]
Semi-supervised Joint Learning for Longitudinal Clinical Events Classification Using Neural Network Models.
W. Tang, J. Ma, A.K. Waljee, and J. Zhu
Stat (2020), e305. (Special issue on deep learning) [Journal]
Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients with Hepatitis C Cirrhosis.
G.N. Ioannou*, W. Tang*, L.A. Beste, M.A. Konerman, G.L. Su, T. Van, E.B. Tapper, A.G. Singal, J. Zhu, and A.K. Waljee
JAMA Network Open (2020), 3(9):e2015626. [Journal]
Digestive Manifestations in Patients Hospitalized with COVID-19.
B.J. Elmunzer and DMC19 Collaborators (on behalf of the North American Alliance for the Study of Digestive Manifestation of COVID-19) [including W. Tang]
Clinical Gastroenterology and Hepatology (2020), 19(7), 1355-1365. [Journal]
A Flexible Generative Framework for Graph-based Semi-supervised Learning.
J. Ma*, W. Tang*, J. Zhu, and Q. Mei
The Annual Conference on Neural Information Processing Systems 32 (2019). (NeurIPS'19). [Proceedings] [Code]