Model-assisted and Knowledge-guided Transfer Regression for the Underrepresented Population.
Zhou, D.#, Li, M.#, Cai, T. and Liu, M., 2024+. In submission. [Arxiv]
Covariate Shift Corrected Conditional Randomization Test.
Xu, B.**, Huang, Y.**, Hong, C., Li, S. and Liu, M., 2024. Conference on Neural Information Processing Systems (NeurIPS). [Arxiv]
Semi-supervised Triply Robust Inductive Transfer Learning.
Cai, T.*, Li, M.* and Liu, M.*, 2024. Journal of the American Statistical Association (forthcoming). [Arxiv]
Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features.
Zhou, D.#, Liu, M.#, Li, M. and Cai, T., 2024. Journal of the American Statistical Association (forthcoming). [Arxiv]
Improve Efficiency of Doubly Robust Estimator when Propensity Score is Misspecified.
Lv, L.** and Liu, M., 2024. Statistics Sinica (forthcoming). [Arxiv]
Multi-source Stable Variable Importance Measure via Adversarial Machine Learning.
Wang, Z.**, Si, N., Guo, Z. and Liu, M., 2024+. In submission. [Arxiv]
Transfer Learning Targeting Mixed Population: A Distributional Robust Perspective.
Zhan, K.**, Xiong, X., Guo, Z., Cai, T. and Liu, M., 2024+. In submission. [Arxiv]
Adaptive and Efficient Learning with Blockwise Missing and Semi-Supervised Data.
Li, Y.**, Yang, X.**, Wei, Y. and Liu, M., 2024+. In submission. [Arxiv]
Efficient Modeling of Surrogates to Improve Multi-source High-dimensional Biobank Studies.
Liu, Y.#, Liu, M.#, Guo, Z. and Cai, T., 2023+. In submission. [Arxiv]
Robust and Efficient Semi-supervised Learning for Ising Model.
Wu, D.** and Liu, M., 2023+. In submission. [Arxiv]
A Semiparametric Approach for Robust and Efficient Learning with Biobank Data.
Liu, M.#, Wang, X.#,** and Hong, C., 2024+. In submission. [Arxiv]
2023
Augmented Transfer Regression Learning with Semi-non-parametric Nuisance Models.
Liu, M., Zhang, Y., Liao, K., Cai, T., 2023. Journal of Machine Learning Research. [Arxiv] [Journal]
Maxway CRT: Improving the Robustness of Model-X Inference.
Li, S.#, and Liu, M.#, 2023. Journal of the Royal Statistical Society Series B (Statistical Methodology) (forthcoming). [Arxiv] [Code] [Journal]
Assessing Heterogeneous Risk of Type II Diabetes Associated with Statin Usage: Evidence from Electronic Health Record Data.
Guo, X.#, Wei, W.#, Liu, M., Cai, T., Wu, C. and Wang, J., 2023. Journal of the American Statistical Association.
[Arxiv] [Journal]
2022
Efficient Estimation and Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling.
Gronsbell, J.#, Liu, M.#, Tian, L. and Cai, T., 2022. Journal of the Royal Statistical Society Series B (Statistical Methodology).
[Arxiv] [Code] [Journal]
Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping.
Zhang, Y.#, Liu, M.#, Neykov, M. and Cai, T., 2022. Journal of Machine Learning Research.
[Arxiv] [Code] [Journal]
Fast and Powerful Conditional Randomization Testing via Distillation.
Liu, M., Katsevich, E., Janson, L. and Ramdas A., 2022. Biometrika. [Arxiv] [Code] [Journal]
Individual Data Protected Integrative Regression Analysis of High-Dimensional Heterogeneous Data.
Cai, T.*, Liu, M.* and Xia, Y.*, 2022. Journal of the American Statistical Association.
[Arxiv] [Code] [Journal]
2021
Double/Debiased Machine Learning for Logistic Partially Linear Model.
Liu, M.*, Zhang, Y.* and Zhou, D.*, 2021. The Econometrics Journal.
[Arxiv] [Code] [Journal]
Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints.
Liu, M., Xia, Y., Cho, K. and Cai, T., 2021. Journal of Machine Learning Research.
[Arxiv] [Journal]
Before 2021
Joint Models for Time-to-Event Data and Longitudinal Biomarkers of High Dimension.
Liu, M., Sun, J., Herazo-Maya, J.D., Kaminski, N. and Zhao, H., 2019.
Statistics in Biosciences. [Journal]
Modeling Individualized Coefficient Alpha to Measure Quality of Test Score Data.
Liu, M., Hu, M. and Zhou, X., 2018. Statistics in medicine. [Journal]
Biomedical Informatics
Heterogeneous associations between interleukin-6 receptor variants and phenotypes across ancestries and implications for therapy.
Wang, X.#, Liu, M.#, Nogues, I.#, et al., 2024. Scientific Reports.
[MedRxiv] [Journal]
Trans-Balance: Reducing demographic disparity for prediction models in the presence of class imbalance.
Hong, C., Liu, M., Wojdyla, D.M., Hickey, J., Pencina, M. and Henao, R., 2024. Journal of Biomedical Informatics.
[Journal]
Knowledge-Driven Online Multimodal Automated Phenotyping System.
Xiong, X.#, Sweet, S.M.#, Liu, M.#, et al., 2023+. In submission.
[MedRxiv]
Weakly Semi-supervised Phenotyping Using Electronic Health Records.
Nogues, I., Wen, J., Lin, Y., Liu, M., Tedeschi, S., Geva, A., Cai, T., Hong, C., 2022. Journal of Biomedical Informatics.
[Journal]
Clinical Knowledge Extraction via Sparse Embedding Regression (KESER) with Multi-Center Large Scale Electronic Health Record Data.
Hong, C., Rush, E., Liu, M., et al., 2021. Nature Portfolio Journal (npj) Digital Medicine.
[MedRxiv] [Webpage] [Journal]
A High-Throughput Phenotyping Algorithm Is Portable from Adult to Pediatric Populations.
Geva, A., Liu, M., Panickan, V., Avillach, P., Cai, T.# and Mandl, K.#, 2021. Journal of the American Medical Informatics Association.
[Journal]
SCRIBE: a new approach to dropout imputation and batch effects correction for single-cell RNA-seq data.
Zhang, Y., Liang, K., Liu, M., Li, Y., Ge, H. and Zhao, H., 2019. Machine Learning in Computational Biology.
[BioRxiv]
Collaborative Research
Diversity and scale: genetic architecture of 2,068 traits in the VA Million Veteran Program.
Verma, A.#, Huffman, J.E.#, Rodriguez, A.#, Conery, M.#, Liu, M.#, et al., 2024. Science (forthcoming).
[MedRxiv]
Changes in laboratory value improvement and mortality rates over the course of the pandemic: an international retrospective cohort study of hospitalised patients infected with SARS-CoV-2.
Hong, C., et al., 2022. BMJ open. [Journal]
International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality.
Weber, G., et al., 2022. Nature Portfolio Journal (npj) Digital Medicine. [MedRxiv] [Journal]
National Trends in Disease Activity for COVID-19 among Children in the US.
Bourgeois, F., Hutch, M., Liu, M., Avillach, P. and Luo, Y. (2021). Frontiers in Pediatrics. [Journal]
International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries.
Bourgeois, F., et al., 2021. JAMA Network. [Journal] (I am listed as the fourth author).
International changes in COVID-19 clinical trajectories across 315 hospitals and 6 countries: retrospective cohort study.
Weber, G., et al., 2021. Journal of Medical Internet Research. [Journal]