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stat.ML2026
Censored Flow Matching for Synthetic Time-to-Event Generation
Jianbin Tan, Xingran Chen, Kevin He +3
Generating time-to-event data is increasingly important in survival analysis, but it remains statistically challenging because event times are often censored and thus prevent flexi…
stat.ME2026
Panel Flow Matching: A Generative Approach to Learning Distributions of Longitudinal Data
Jianbin Tan, Pixu Shi, Anru R. Zhang
Learning distributions of longitudinal data is central to tasks such as visualization, completion, classification, and synthetic data generation, but it remains statistically chall…
stat.ME2026
Associating High-Dimensional Longitudinal Datasets through an Efficient Cross-Covariance Decomposition
Jianbin Tan, Pixu Shi
Understanding associations between paired high-dimensional longitudinal datasets is a fundamental yet challenging problem arising across scientific domains, including longitudinal…