5 papers
Structure-based Transfer Learning
Ho Yi Alexis Ho, Xinzhou Guo, Shuoxun Xu
Transfer learning improves estimation in a target study using information from related sources. Classical transfer learning is typically data-based, requiring access to the source…
Stein-Encoder: A White-Box Supervised Encoder via Stein Identities in Multi-Modal Studies
Jiarui Zhang, Shuoxun Xu, Jiasheng Shi +1
In multi-modal biomedical research, integrating high-dimensional genomic data with clinical baselines is essential for precision medicine. However, standard deep neural network app…
Multi-Objective Learning for Diffusion Models: A Statistical Theory under Semi-Supervised Learning
Ziheng Cheng, Yixiao Huang, Hanlin Zhu +5
Diffusion models are increasingly used as powerful conditional generators, yet real deployments often involve multiple target distributions arising from different tasks, e.g., dive…
In-Sample Evaluation of Subgroups Identified by Generic Machine Learning
Shuoxun Xu, Xinzhou Guo
When a subgroup is identified from the data, it must be evaluated in a replicable way. The usual in-sample approach, which evaluates the post-hoc identified subgroup as predefined,…
Differentially Private Multimodal Laplacian Dropout (DP-MLD) for EEG Representative Learning
Xiaowen Fu, Bingxin Wang, Xinzhou Guo +2
Recently, multimodal electroencephalogram (EEG) learning has shown great promise in disease detection. At the same time, ensuring privacy in clinical studies has become increasingl…