Jin-Hong Du

Jin-Hong Du

PhD student at Carnegie Mellon University

Publications

Preprint

  1. Jin-Hong Du, Zhenghao Zeng, Edward H. Kennedy, Larry Wasserman, and Kathryn Roeder. "Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes". 2024. [arXiv]
  2. Haeun Moon, Jin-Hong Du, Jing Lei, and Kathryn Roeder. "Augmented Doubly Robust Post-Imputation Inference for Proteomic Data". 2024. [arXiv] [Github]
  3. †Pierre C. Bellec, Jin-Hong Du, Takuya Koriyama, Pratik Patil, and Kai Tan. "Corrected generalized cross-validation for finite ensembles of penalized estimators". 2023. [arXiv] [Github]
  4. Jin-Hong Du, Larry Wasserman, and Kathryn Roeder. "Simultaneous inference for generalized linear models with unmeasured confounders". 2023. [arXiv] [Github]
  5. Jin-Hong Du*, Tianyu Chen*, Ming Gao, and Jingshu Wang. "Joint Trajectory Inference for Single-cell Genomics Using Deep Learning with a Mixture Prior". 2020. [bioRXiv] [Github]

Journal

  1. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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

  1. 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. (accepted) 2024. [arXiv] [Github]
  2. 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]
  3. 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]
  4. Jin-Hong Du. "Model-Based Trajectory Inference for Single-Cell RNA Sequencing Using Deep Learning with a Mixture Prior". Paper presented at the MCBIOS & MAQC 2021 Joint Conference. 26-30 April 2021.
  5. 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 World Congress. sn. 2020.

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.