15 citations · 62 across the 21 of their papers we have counts for
21 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…
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 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…
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…
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…
Improve Efficiency of Doubly Robust Estimator when Propensity Score is Misspecified
Liangbo Lyu, Molei Liu
Doubly robust (DR) estimation is a crucial technique in causal inference and missing data problems. We propose a novel Propensity score Augmentved Doubly robust (PAD) estimator to…
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…
Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features
Doudou Zhou, Molei Liu, Mengyan Li +1
Due to label scarcity and covariate shift happening frequently in real-world studies, transfer learning has become an essential technique to train models generalizable to some targ…
Assessing the Most Vulnerable Subgroup to Type II Diabetes Associated with Statin Usage: Evidence from Electronic Health Record Data
Xinzhou Guo, Waverly Wei, Molei Liu +3
There have been increased concerns that the use of statins, one of the most commonly prescribed drugs for treating coronary artery disease, is potentially associated with the incre…
Maxway CRT: Improving the Robustness of the Model-X Inference
Shuangning Li, Molei Liu
The model-X conditional randomization test (CRT) is a flexible and powerful testing procedure for the conditional independence hypothesis: X is independent of Y conditioning on Z.…
Efficient Estimation and Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling
Jessica Gronsbell, Molei Liu, Lu Tian +1
In many contemporary applications, large amounts of unlabeled data are readily available while labeled examples are limited. There has been substantial interest in semi-supervised…
Augmented Transfer Regression Learning with Semi-non-parametric Nuisance Models
Molei Liu, Yi Zhang, Katherine P Liao +1
In contemporary statistical learning, covariate shift correction plays an important role in transfer learning when distribution of the testing data is shifted from the training dat…
Double/Debiased Machine Learning for Logistic Partially Linear Model
Molei Liu, Yi Zhang, Doudou Zhou
We propose double/debiased machine learning approaches to infer (at the parametric rate) the parametric component of a logistic partially linear model with the binary response foll…
A Note on Debiased/Double Machine Learning Logistic Partially Linear Model
Molei Liu
It is of particular interests in many application fields to draw doubly robust inference of a logistic partially linear model with the predictor specified as combination of a targe…
Fast and Powerful Conditional Randomization Testing via Distillation
Molei Liu, Eugene Katsevich, Lucas Janson +1
We consider the problem of conditional independence testing: given a response Y and covariates (X,Z), we test the null hypothesis that Y is independent of X given Z. The conditiona…
Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints
Molei Liu, Yin Xia, Kelly Cho +1
Identifying informative predictors in a high dimensional regression model is a critical step for association analysis and predictive modeling. Signal detection in the high dimensio…
Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping
Yichi Zhang, Molei Liu, Matey Neykov +1
Electronic Health Records (EHR) data, a rich source for biomedical research, have been successfully used to gain novel insight into a wide range of diseases. Despite its potential,…
Individual Data Protected Integrative Regression Analysis of High-dimensional Heterogeneous Data
Tianxi Cai, Molei Liu, Yin Xia
Evidence-based decision making often relies on meta-analyzing multiple studies, which enables more precise estimation and investigation of generalizability. Integrative analysis of…
Modeling Coefficient Alpha for Measurement of Individualized Test Score Internal Consistency
Molei Liu, Ming Hu, Xiaohua Zhou
A method for measuring individualized reliability of several tests on subjects with heterogenecity is proposed. A regression model is developed based on three sets of generalized e…
Joint Models for Time-to-Event Data and Longitudinal Biomarkers of High Dimension
Molei Liu, Jiehuan Sun, Jose D. Herazo-Maya +2
Joint models for longitudinal biomarkers and time-to-event data are widely used in longitudinal studies. Many joint modeling approaches have been proposed to deal with different ty…