Transfer learning is crucial for training models that generalize to unlabeled target
populations using labeled source data, especially in real-world studies where label scarcity …

Efficient and multiply robust risk estimation under general forms of dataset shift Page 1 The
Annals of Statistics 2024, Vol. 52, No. 4, 1796–1824 https://doi.org/10.1214/24-AOS2422 © …

Introduction: Risk prediction, including early disease detection, prevention, and intervention,
is essential to precision medicine. However, systematic bias in risk estimation caused by …

Leveraging machine-learning methods to predict outcomes on some unlabeled datasets
and then using these pseudo-outcomes in subsequent statistical inference is common in …

Large-scale genomics data combined with Electronic Health Records (EHRs) illuminate the
path towards personalized disease management and enhanced medical interventions …

In modern data analysis, it is common to use machine learning methods to predict outcomes
on unlabelled datasets and then use these pseudo‐outcomes in subsequent statistical …

Covariate shift and outcome model heterogeneity are two prominent challenges in
leveraging external sources to improve risk modeling for underrepresented cohorts in …

Estimating comparison functions is crucial in numerous domains, such as econometrics,
clinical medicine, and public health, where evaluating the effectiveness of interventions or …

Despite recent advances in transfer learning with multiple source data sets, there still lacks
developments for mixture target populations that could be approximated through a …

We develop an augmented transfer regression learning (ATReL) approach that introduces
an imputation model to augment the importance weighting equation to achieve double …