4 citations · 21 across the 12 of their papers we have counts for
8 papers · 1 filter
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…
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…
Robust Inference for Federated Meta-Learning
Zijian Guo, Xiudi Li, Larry Han +1
Synthesizing information from multiple data sources is critical to ensure knowledge generalizability. Integrative analysis of multi-source data is challenging due to the heterogene…
Towards Optimal Use of Surrogate Markers to Improve Power
Xuan Wang, Layla Parast, Lu Tian +1
Motivated by increasing pressure for decision makers to shorten the time required to evaluate the efficacy of a treatment such that treatments deemed safe and effective can be made…
Semi-supervised Triply Robust Inductive Transfer Learning
Tianxi Cai, Mengyan Li, Molei Liu
In this work, we propose a Semi-supervised Triply Robust Inductive transFer LEarning (STRIFLE) approach, which integrates heterogeneous data from a label-rich source population and…
Semi-supervised Transfer Learning for Evaluation of Model Classification Performance
Linshanshan Wang, Xuan Wang, Katherine P. Liao +1
In modern machine learning applications, frequent encounters of covariate shift and label scarcity have posed challenges to robust model training and evaluation. Numerous transfer…