4 papers
A Communication-Efficient Distributed Algorithm for Learning with Heterogeneous and Structurally Incomplete Multi-Site Data
Xiaokang Liu, Yuchen Yang, Yifei Sun +4
In multicenter biomedical research, integrating data from multiple decentralized sites provides more robust and generalizable findings due to its larger sample size and the ability…
Targeted learning via probabilistic subpopulation matching
Xiaokang Liu, Jie Hu, Naimin Jing +4
In biomedical research, to obtain more accurate prediction results from a target study, leveraging information from multiple similar source studies is proved to be useful. However,…
Distributed inference for heterogeneous mixture models using multi-site data
Xiaokang Liu, Rui Duan, Raymond J. Carroll +2
Mixture models postulate the overall population as a mixture of finite subpopulations with unobserved membership. Fitting mixture models usually requires large sample sizes and com…
Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras
Jun Yu, Yutong Dai, Xiaokang Liu +14
MTL is a learning paradigm that effectively leverages both task-specific and shared information to address multiple related tasks simultaneously. In contrast to STL, MTL offers a s…