Transfer learning uses a data model, trained to make predictions or inferences on data from one population, to make reliable predictions or inferences on data from another population. Most existing transfer learning approaches are based on fine-tuning pre-trained neural network models, and fail to provide crucial uncertainty quantification. We develop a statistical framework for model predictions based on transfer learning, called RECaST. The primary mechanism is a Cauchy random effect that recalibrates a source model to a target population; we mathematically and empirically demonstrate the validity of our RECaST approach for transfer learning between linear models, in the sense that prediction sets will achieve their nominal stated coverage, and we numerically illustrate the method's robustness to asymptotic approximations for nonlinear models. Whereas many existing techniques are built on particular source models, RECaST is agnostic to the choice of source model. For example, our RECaST transfer learning approach can be applied to a continuous or discrete data model with linear or logistic regression, deep neural network architectures, etc. Furthermore, RECaST provides uncertainty quantification for predictions, which is mostly absent in the literature. We examine our method's performance in a simulation study and in an application to real hospital data.
翻译:我们开发了一个基于转移学习的模型预测的统计框架,称为RECaST。主要机制是将源模型对目标人群进行重新校正的怪异随机效应;我们数学和实验性地展示了我们RECAST方法在线性模型之间转移学习的有效性,即预测组将达到其标称的覆盖范围,我们从数字上展示了该方法对非线性模型的稳健性和非线性近似值。虽然许多现有技术是建立在特定源模型上,但RECAST对选择源模型是不可知的。例如,我们的RECAST转移学习方法可以适用于具有线性或物流回归、深层神经网络结构等的连续或离散数据模型。此外,RECAST提供了该方法对于非线性模型的稳健性和非线性近似值。我们在医院性模型中进行一种不确定性的定量分析,我们用在实际的模型中,我们用的是一种不精确的模型来分析。