activity
20222024
most citedDoubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features

4 citations · 5 across the 7 of their papers we have counts for

collaborators

7 papers

stat.ML2024

Federated One-Shot Ensemble Clustering

Rui Duan, Xin Xiong, Jueyi Liu +2

Cluster analysis across multiple institutions poses significant challenges due to data-sharing restrictions. To overcome these limitations, we introduce the Federated One-shot Ense…

stat.ME2023

Semi-supervised Estimation of Event Rate with Doubly-censored Survival Data

Yang Wang, Qingning Zhou, Tianxi Cai +1

Electronic Health Record (EHR) has emerged as a valuable source of data for translational research. To leverage EHR data for risk prediction and subsequently clinical decision supp…

cs.LG2023

Distributionally Robust Transfer Learning

Xin Xiong, Zijian Guo, Tianxi Cai

Many existing transfer learning methods rely on leveraging information from source data that closely resembles the target data. However, this approach often overlooks valuable know…

stat.ML2023

Knowledge Graph Embedding with Electronic Health Records Data via Latent Graphical Block Model

Junwei Lu, Jin Yin, Tianxi Cai

Due to the increasing adoption of electronic health records (EHR), large scale EHRs have become another rich data source for translational clinical research. Despite its potential,…

cs.AI2023

LATTE: Label-efficient Incident Phenotyping from Longitudinal Electronic Health Records

Jun Wen, Jue Hou, Clara-Lea Bonzel +14

Electronic health record (EHR) data are increasingly used to support real-world evidence (RWE) studies. Yet its ability to generate reliable RWE is limited by the lack of readily a…

stat.ME20221 cited

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…