Algorithmic fairness in artificial intelligence for medicine and healthcare
In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
Explainable, domain-adaptive, and federated artificial intelligence in medicine
Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in
each domain is driven by a growing body of annotated data, increased computational …
each domain is driven by a growing body of annotated data, increased computational …
Algorithm fairness in ai for medicine and healthcare
In the current development and deployment of many artificial intelligence (AI) systems in
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …
Multiply robust federated estimation of targeted average treatment effects
Federated or multi-site studies have distinct advantages over single-site studies, including
increased generalizability, the ability to study underrepresented populations, and the …
increased generalizability, the ability to study underrepresented populations, and the …
Federated causal inference in heterogeneous observational data
We are interested in estimating the effect of a treatment applied to individuals at multiple
sites, where data is stored locally for each site. Due to privacy constraints, individual‐level …
sites, where data is stored locally for each site. Due to privacy constraints, individual‐level …
Semi-supervised triply robust inductive transfer learning
In this work, we propose a Semi-supervised Triply Robust Inductive transFer LEarning
(STRIFLE) approach, which integrates heterogeneous data from a label-rich source …
(STRIFLE) approach, which integrates heterogeneous data from a label-rich source …
Robust angle-based transfer learning in high dimensions
Transfer learning improves target model performance by leveraging data from related
source populations, especially when target data are scarce. This study addresses the …
source populations, especially when target data are scarce. This study addresses the …
A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data
Transfer learning aims to integrate useful information from multi-source datasets to improve
the learning performance of target data. This can be effectively applied in genomics when …
the learning performance of target data. This can be effectively applied in genomics when …
Learning from similar linear representations: Adaptivity, minimaxity, and robustness
Representation multi-task learning (MTL) and transfer learning (TL) have achieved
tremendous success in practice. However, the theoretical understanding of these methods is …
tremendous success in practice. However, the theoretical understanding of these methods is …
Multi-task learning with summary statistics
Multi-task learning has emerged as a powerful machine learning paradigm for integrating
data from multiple sources, leveraging similarities between tasks to improve overall model …
data from multiple sources, leveraging similarities between tasks to improve overall model …