---
abstract: 'Jointly using data from multiple similar sources for the training of prediction models is increasingly becoming an important task in many fields of science. In this paper, we propose a framework for [*generalist and specialist*]{} predictions that leverages multiple datasets, with potential heterogenity in the relationships between predictors and outcomes. Our framework uses ensembling with stacking, and includes three major components: 1) training of the ensemble members using one or more datasets, 2) a no-data-reuse technique for stacking weights estimation and 3) task-specific utility functions. We prove that under certain regularity conditions, our framework produces a stacked prediction function with oracle property. We also provide analytically the conditions under which the proposed no-data-reuse technique will increase the prediction accuracy of the stacked prediction function compared to using the full data. We perform a simulation study to numerically verify and illustrate these results and apply our framework to predicting mortality based on a collection of variables including long-term exposure to common air pollutants.'
author:
- Boyu Ren
- Prasad Patil
- Francesca Dominici
- Giovanni Parmigiani
- Lorenzo Trippa
bibliography:
- 'reference.bib'
title: |
    Cross-study Learning\
    for Generalist and Specialist Predictions
---

Introduction
============

New advances in technologies, for 