4 citations · 16 across the 8 of their papers we have counts for
8 papers
Hierarchical Pretraining for Biomedical Term Embeddings
Bryan Cai, Sihang Zeng, Yucong Lin +3
Electronic health records (EHR) contain narrative notes that provide extensive details on the medical condition and management of patients. Natural language processing (NLP) of cli…
Consensus Knowledge Graph Learning via Multi-view Sparse Low Rank Block Model
Tianxi Cai, Dong Xia, Luwan Zhang +1
Network analysis has been a powerful tool to unveil relationships and interactions among a large number of objects. Yet its effectiveness in accurately identifying important node-n…
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…
Federated Offline Reinforcement Learning
Doudou Zhou, Yufeng Zhang, Aaron Sonabend-W +3
Evidence-based or data-driven dynamic treatment regimes are essential for personalized medicine, which can benefit from offline reinforcement learning (RL). Although massive health…
Asymptotic Distribution-free Change-point Detection for Modern Data Based on a New Ranking Scheme
Doudou Zhou, Hao Chen
Change-point detection (CPD) involves identifying distributional changes in a sequence of independent observations. Among nonparametric methods, rank-based methods are attractive d…
A new ranking scheme for modern data and its application to two-sample hypothesis testing
Doudou Zhou, Hao Chen
Rank-based approaches are among the most popular nonparametric methods for univariate data in tackling statistical problems such as hypothesis testing due to their robustness and e…
Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices
Doudou Zhou, Tianxi Cai, Junwei Lu
Matrix completion has attracted attention in many fields, including statistics, applied mathematics, and electrical engineering. Most of the works focus on the independent sampling…
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…