Prediction-powered inference is a framework for performing valid statistical inference when
an experimental dataset is supplemented with predictions from a machine-learning system …

This supplementary material provides the complete proofs of the main theorems and
technical results introduced in the paper,“Transfer Learning for Functional Mean Estimation …

Transfer learning provides a powerful tool for incorporating data from related studies into a
target study of interest. In epidemiology and medical studies, the classification of a target …

The main challenge that sets transfer learning apart from traditional supervised learning is
the distribution shift, reflected as the shift between the source and target models and that …

The limited representation of minorities and disadvantaged populations in large-scale
clinical and genomics research poses a significant barrier to translating precision medicine …

In this work, we propose a Semi-supervised Triply Robust Inductive transFer LEarning
(STRIFLE) approach, which integrates heterogeneous data from a label-rich source …

Many existing two-phase kernel-based hypothesis transfer learning algorithms employ the
same kernel regularization across phases and rely on the known smoothness of functions to …

In this article, we focus on prediction of a target model by transferring the information of
source models. To be flexible, we use semiparametric additive frameworks for the target and …

In dynamic decision-making scenarios across business and healthcare, leveraging sample
trajectories from diverse populations can significantly enhance reinforcement learning (RL) …

Synthesizing information from multiple data sources is critical to ensure knowledge
generalizability. Integrative analysis of multi-source data is challenging due to the …