Many existing transfer learning methods rely on leveraging information f...
Due to the increasing adoption of electronic health records (EHR), large...
Electronic health record (EHR) data are increasingly used to support
rea...
Surrogate variables in electronic health records (EHR) play an important...
Synthesizing information from multiple data sources is critical to ensur...
While randomized controlled trials (RCTs) are the gold-standard for
esta...
Network analysis has been a powerful tool to unveil relationships and
in...
Motivated by increasing pressure for decision makers to shorten the time...
The primary benefit of identifying a valid surrogate marker is the abili...
In this work, we propose a semi-supervised triply robust inductive trans...
In modern machine learning applications, frequent encounters of covariat...
Due to label scarcity and covariate shift happening frequently in real-w...
Evidence-based or data-driven dynamic treatment regimes are essential fo...
There have been increased concerns that the use of statins, one of the m...
Objective: Disease knowledge graphs are a way to connect, organize, and
...
Federated learning of causal estimands may greatly improve estimation
ef...
Data from both a randomized trial and an observational study are sometim...
Large health care data repositories such as electronic health records (E...
A notable challenge of leveraging Electronic Health Records (EHR) for
tr...
Large clinical datasets derived from insurance claims and electronic hea...
The limited representation of minorities and disadvantaged populations i...
Matrix completion has attracted attention in many fields, including
stat...
Risk modeling with EHR data is challenging due to a lack of direct
obser...
Shortcomings of randomized clinical trials are pronounced in urgent heal...
Nested case-control (NCC) is a sampling method widely used for developin...
In the last decade, the secondary use of large data from health systems,...
Readily available proxies for time of disease onset such as time of the ...
Reinforcement learning (RL) has shown great success in estimating sequen...
In many contemporary applications, large amounts of unlabeled data are
r...
Importance weighting is naturally used to adjust for covariate shift.
Ho...
Offline Reinforcement Learning (RL) is a promising approach for learning...
Identifying informative predictors in a high dimensional regression mode...
Electronic Health Records (EHR) data, a rich source for biomedical resea...
The ability to predict individualized treatment effects (ITEs) based on ...
Meta-analyzing multiple studies, enabling more precise estimation and
in...
Motivated by a series of applications in data integration, language
tran...
Despite recent development in methodology, community detection remains a...
Word embeddings have emerged as a popular approach to unsupervised learn...
We propose a computationally and statistically efficient divide-and-conq...
There is strong interest in conducting comparative effectiveness researc...
Commonly in biomedical research, studies collect data in which an outcom...
In many modern machine learning applications, the outcome is expensive o...
We consider the recovery of regression coefficients, denoted by
β_0, for...
We consider the linear regression problem under semi-supervised settings...
It is known that for a certain class of single index models (SIMs) Y =
f...
Matrix completion has attracted significant recent attention in many fie...