7 papers
Multimodal domain adaptation under label shift and blockwise missing modalities
Zebin Wang, Ziang Dou, Molei Liu +1
Multimodal domain adaptation uses labeled source datasets to predict outcomes in an unlabeled target population. Here, different sources may observe different subsets of modalities…
Domain Adaptation Targeting Heterogeneous and Imbalanced Subgroups
Doudou Zhou, Mengyan Li, Yun Wang +2
Domain adaptation enables generalizable and efficient data-driven research. However, existing work has largely focused on domain adaptation for some intrinsically homogeneous targe…
Efficient Modeling of Surrogates to Improve Multi-source High-dimensional Integrative Regression
Yue Liu, Molei Liu, Zijian Guo +1
Surrogate variables play an important role in various fields due to the scarcity or absence of gold standard labels. We develop a novel approach named SASH for Surrogate-Assisted a…
CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition
Zebin Wang, Menghan Lin, Bolin Shen +4
Graph Neural Networks (GNNs) have demonstrated remarkable utility across diverse applications, and their growing complexity has made Machine Learning as a Service (MLaaS) a viable…
Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data
Linshanshan Wang, Mengyan Li, Zongqi Xia +2
Electronic Health Records (EHR) offer rich real-world data for personalized medicine, providing insights into disease progression, treatment responses, and patient outcomes. Howeve…
Domain Adaptation Optimized for Robustness in Mixture Populations
Keyao Zhan, Xin Xiong, Zijian Guo +2
While domain adaptation methods address data shifts, most assume target populations align with at least one source population, neglecting mixtures that combine sources influenced b…