Jin-Hong Du

Jin-Hong Du

PhD student at Carnegie Mellon University

Publications

Preprint

  1. Jin-Hong Du, Kathryn Roeder, and Larry Wasserman. "Assumption-lean post-integrated inference with negative control outcomes". 2024. [arXiv]
  2. Pratik Patil, Jin-Hong Du, and Ryan J. Tibshirani. "Revisiting optimism and model complexity in the wake of overparameterized machine learning". 2024. [arXiv] [Github]
  3. Takuya Koriyama, Pratik Patil, Jin-Hong Du, Kai Tan, and Pierre C. Bellec. "Precise asymptotics of bagging regularized M-estimators". 2024. [arXiv] [Github]
  4. Yaoming Zhen, and Jin-Hong Du. "Network-based neighborhood regression". 2024. [arXiv]
  5. Jin-Hong Du, Zhenghao Zeng, Edward H. Kennedy, Larry Wasserman, and Kathryn Roeder. "Causal inference for genomic data with multiple heterogeneous outcomes". 2024. [arXiv] [Github]
  6. Haeun Moon, Jin-Hong Du, Jing Lei, and Kathryn Roeder. "Augmented doubly robust post-imputation inference for proteomic data". 2024. [arXiv] [Github]
  7. Jin-Hong Du, Larry Wasserman, and Kathryn Roeder. "Simultaneous inference for generalized linear models with unmeasured confounders". 2023. [arXiv] [Github]

Journal

  1. †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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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 (accepted). 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.