Publications

Preprint

  1. Jin-Hong Du, Maya Shen, Hansruedi Mathys, and Kathryn Roeder. "Causal differential expression analysis under unmeasured confounders with causarray". 2025. bioRxiv
  2. Jin-Hong Du, Zhenghao Zeng, Edward H. Kennedy, Larry Wasserman, and Kathryn Roeder. "Causal inference for genomic data with multiple heterogeneous outcomes". Minor revision at Journal of the American Statistical Association (2024). [arXiv] [Github]
  3. Jin-Hong Du, Kathryn Roeder, and Larry Wasserman. "Assumption-lean post-integrated inference with negative control outcomes". 2024. [arXiv] [Github]
  4. Pratik Patil, Jin-Hong Du, and Ryan J. Tibshirani. "Revisiting optimism and model complexity in the wake of overparameterized machine learning". 2024. [arXiv] [Github]
  5. Takuya Koriyama, Pratik Patil, Jin-Hong Du, Kai Tan, and Pierre C. Bellec. "Precise asymptotics of bagging regularized M-estimators". 2024. [arXiv] [Github]
  6. Yaoming Zhen, and Jin-Hong Du. "Network-based neighborhood regression". 2024. [arXiv]
  7. Jin-Hong Du, Larry Wasserman, and Kathryn Roeder. "Simultaneous inference for generalized linear models with unmeasured confounders". 2023. [arXiv] [Github]

Journal

  1. Haeun Moon, Jin-Hong Du, Jing Lei, and Kathryn Roeder. "Augmented doubly robust post-imputation inference for proteomic data". In: The Annals of Applied Statistics (accepted) (2025). [arXiv] [Github]
  2. †Pierre C. Bellec, Jin-Hong Du, Takuya Koriyama, Pratik Patil, and Kai Tan. "Corrected generalized cross-validation for finite ensembles of penalized estimators". In: Journal of the Royal Statistical Society: Series B (2024). [arXiv] [DOI] [Github]
  3. Jin-Hong Du*, Tianyu Chen*, Ming Gao, and Jingshu Wang. "Joint trajectory inference for single-cell genomics using deep learning with a mixture prior". In: Proceedings of the National Academy of Sciences (2024). [bioRXiv] [DOI] [Github]
  4. Jin-Hong Du, Pratik Patil, Kathryn Roeder, and Arun Kumar Kuchibhotla, "Extrapolated cross-validation for randomized ensembles". In: Journal of Computational and Graphical Statistics (2023). [arXiv] [DOI] [Github]
  5. Pratik Patil*, Jin-Hong Du*, and Arun Kumar Kuchibhotla, "Bagging in overparameterized learning: risk characterization and risk monotonization". In: Journal of Machine Learning Research (2023). [arXiv] [JMLR] [Github]
  6. Jin-Hong Du, Zhanrui Cai, and Kathryn Roeder. "Robust probabilistic modeling for single-cell multimodal mosaic integration and imputation via scVAEIT". In: Proceedings of the National Academy of Sciences (2022). [bioRXiv] [DOI] [Github]
  7. Jin-Hong Du*, Yifeng Guo*, and Xueqin Wang. "High-dimensional portfolio selection with cardinality constraints". In: Journal of the American Statistical Association (2022). [arXiv] [DOI] [Github]
  8. Jose Israel Rodriguez, Jin-Hong Du, Yiling You, and Lek-Heng Lim. "Fiber product homotopy method for multiparameter eigenvalue problems". In: Numerische Mathematik (2021). [arXiv] [DOI] [Github]

Conference / Workshop

  1. Jin-Hong Du, and Pratik Patil. "Implicit regularization paths of weighted neural representations". In: Thirty-eighth Conference on Neural Information Processing Systems. 2024. [arXiv] [Github]
  2. Wenbin Zhou, and Jin-Hong Du, "Distance-preserving generative modeling of spatial transcriptomics". In: 23rd International Workshop on Data Mining in Bioinformatics (BIOKDD). 2024. [arXiv]
  3. Pratik Patil, Jin-Hong Du, and Ryan J. Tibshirani. "Optimal ridge regularization for out-of-distribution prediction". In: Proceedings of the 41th International Conference on Machine Learning. 2024. (spotlight) [PMLR] [arXiv] [Github]
  4. Pratik Patil, and Jin-Hong Du, "Generalized equivalences between subsampling and ridge regularization". In: Thirty-seventh Conference on Neural Information Processing Systems. 2023. [arXiv] [Github]
  5. Jin-Hong Du*, Pratik Patil*, and Arun Kumar Kuchibhotla. "Subsample ridge ensembles: equivalences and generalized cross-validation". In: Proceedings of the 40th International Conference on Machine Learning. 23–29 Jul 2023. (oral) [PMLR] [arXiv] [Github]

Interdisciplinary Research

  1. Zhen Yang, Jin-Hong Du, Yiting Lin, Zhen Du, Li Xia, Qianchuan Zhao, and Xiaohong Guan. "Increasing the energy efficiency of a data center based on machine learning". In: Journal of Industrial Ecology (2021). [DOI]
  2. Rutong Zeng, Xiang Zhang, Chushan Zheng, Jin-Hong Du, Zixiong Gao, Jun Wei, Jun Shen, and Yao Lu. "Decoupling convolution network for characterizing the metastatic lymph nodes of breast cancer patients". In: Medical Physics (2021) [DOI]
  3. Jing-Xian Tang*, Jin-Hong Du*, Yiting Lin, and Qing-Shan Jia. "Predictive maintenance of VRLA batteries in UPS towards reliable data centers". In: IFAC-PapersOnLine 53.2 (2020), pp. 13607–13612. ISSN: 2405-8963. [DOI]
  4. Xingyu Fu, Jin-Hong Du, Yifeng Guo, Mingwen Liu, Tao Dong, and Xiuwen Duan. "A machine learning framework for stock selection". 2018. [arXiv]

* Equal contributions.
† Alphabetically ordered.