Publications
Peer-Reviewed Journal Articles
Association and independence test for random objects. Hang Zhou and Hans-Georg Müller. Annals of Statistics, (2026). [journal][BibTeX][code]
Non-Euclidean data analysis with metric statistics. Wookyeong Song, Hang Zhou, Yidong Zhou and Hans-Georg Müller. Harvard Data Science Review, (2026). [journal][BibTeX][code]
Intrinsic correlation analysis for Wasserstein functional data. Hang Zhou, Zhenhua Lin and Fang Yao. Statistica Sinica, (2026). [journal][pdf][BibTeX][code]
Wasserstein-Fréchet integration of conditional distributions. Álvaro Gajardo, Hans-Georg Müller and Hang Zhou. Electronic Journal of Statistics, (2025). [journal][BibTeX]
Theory of functional principal component analysis for noisy and discretely observed data. Hang Zhou, Dongyi Wei and Fang Yao. Annals of Statistics, (2025). [journal][BibTeX]
Conformal inference for random objects. Hang Zhou and Hans-Georg Müller. Annals of Statistics, (2025). [journal][BibTeX][code]
Deep regression for repeated measurements. Shunxing Yan, Fang Yao and Hang Zhou. Journal of American Statistical Association, (2025). [journal][BibTeX]
Hope: A hierarchical perspective for semi-supervised 2D-3D cross-Modal retrieval. Fan Zhang, Hang Zhou, Xian-Sheng Hua, Chong Chen and Xiao Luo. IEEE Transactions on Pattern Analysis and Machine Intelligence, (2024). [journal][BibTeX]
Detecting errors in numerical data via any regression model. Hang Zhou, Jonas Mueller, Mayank Kumar, Jane-Ling Wang and Jing Lei. Journal of Data-centric Machine Learning Research, (2024). [journal][BibTeX][code]
Functional linear regression for discretely observed data: from ideal to reality. Hang Zhou, Fang Yao and Huiming Zhang. Biometrika, (2023). [journal][BibTeX][code]
Conference Papers
Deep neural network regression with functional covariates. Hang Zhou, Ju-Sheng Hong, Xiucai Ding and Jane-Ling Wang. In The Forty-third International Conference on Machine Learning, (2026). [ICML26][BibTeX]
CB-CV: A cluster-based cross-validation benchmark for multimodal video out-of-distribution detection. Ji Zhang, Xiao Luo and Hang Zhou. In The Forty-third International Conference on Machine Learning, (2026). [ICMR26][BibTeX]
CODE: Towards Partial Label Graph Learning via Coupled Dual Separation. Yiyang Gu, Taian Guo, Hang Zhou, Zihao Chen, Zhiping Xiao, Yifang Qin, Xiao Luo, Wei Ju, Yifan Wang, Ming Zhang. In Proceedings of the 33rd ACM International Conference on Multimedia, (2025). [ACMMM][BibTeX]
Future Matters for Present: Towards Effective Physical Simulation over Meshes Xiao Luo, Junyu Luo, Huiyu Jiang, Hang Zhou, Zhiping Xiao, Wei Ju, Carl Yang, Ming Zhang, Yizhou Sun. In ACM Conference on Knowledge Discovery and Data Mining, (2025). [KDD25][BibTeX]
PGODE: Towards high-quality system dynamics modeling. Xiao Luo, Yiyang Gu, Huiyu Jiang, Hang Zhou, Jinsheng Huang, Wei Ju, Zhiping Xiao, Ming Zhang, and Yizhou Sun. In The Forty-first International Conference on Machine Learning, (2024). [ICML24][BibTeX]
EGODE: An Event-attended Graph ODE Framework for Modeling Rigid Dynamics. Jingyang Yuan, Gongbo Sun, Zhiping Xiao, Hang Zhou, Xiao Luo, Junyu Luo, Yusheng Zhao, Wei Ju, Ming Zhang. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, (2024). [NeurIPS24][BibTeX]