About Me
I am an assistant professor of the Dr. Bing Zhang Department of Statistics, University of Kentucky, Lexington. My research interests include machine learning, privacy-aware decentralized learning, deep learning, statistical inference, model selection and diagnostics.
Publications
J. Zhang, Y. Yang, J. Ding, Additive-effect assisted learning
Journal of the Royal Statistical Society Series B: Statistical Methodology 88.2 (2026): 657-676, link
J. Zhang, J. Ding, Y. Yang, Target cross-validation
Bernoulli 29 (1) (2023), 377--402, link
J. Zhang, J. Ding, Y. Yang, Is a classification procedure good enough? a goodness-of-fit assessment tool for classification learning
Journal of the American Statistical Association 118.542 (2023): 1115-1125, link
X. Wang, J. Zhang, M. Hong, Y. Yang, J. Ding, Parallel assisted learning
IEEE Transactions on Signal Processing 70 (2022), 5848--5858, link
J. Zhang, Y. Yang, J. Ding, Information criteria for model selection
Wiley Interdisciplinary Reviews: Computational Statistics (2023): e1607, link
E. Diao, G. Wang, J. Zhang, Y. Yang, J. Ding, V. Tarokh, Pruning deep neural networks from a sparsity perspective
submitted to ICLR 2023, accepted
C. Chen, J. Zhang (co‐first author), J. Ding, Y. Zhou, Assisted learning with unsupervised domain adaptation
Abstracts of papers IEEE International Symposium on Information Theory. 2023, link
X. Tang, J. Zhang (co-first author), Y. He, X. Zhang, Z. Lin, S. Partarrieu, E. B. Hanna, Z. Ren, Y. Yang, X. Wang, N. Li, J. Ding, J. Liu, Multi-task learning for single-cell multi-modality biology
Nature Communications 14, 2546 (2023), pdf