Model Linkage Selection for Cooperative Learning
Model Linkage Selection for Cooperative Learning
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发表时间：
2020-05
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通讯作者：
Jiaying Zhou;Jie Ding;Kean Ming Tan;V. Tarokh
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作者：
Jiaying Zhou;Jie Ding;Kean Ming Tan;V. Tarokh
数据收集设备和计算平台的快速发展在许多科学领域产生了大量的学习者和数据模式。我们考虑了每个学习器拥有一对参数统计模型和特定数据源的设置，目标是整合一组学习器的信息，以提高特定学习器的预测准确性。整合信息的一种自然方法是在一组具有共同兴趣参数的学习者之间建立一个联合模型。然而，一组学习器之间的参数共享模式是未知的。错误地指定每个学习者的参数共享模式和参数统计模型会产生有偏差的估计量，并降低联合模型的预测精度。在本文中，我们提出了一种新的框架，用于跨一组学习器集成信息，该框架对模型错误规范和错误指定的参数共享模式具有鲁棒性。主要的关键是顺序地合并额外的学习器，这些学习器可以提高现有联合模型的预测精度，该模型基于一组学习器中用户指定的参数共享模式，从具有一个学习器的模型开始。理论上，我们证明了该方法可以基于用户指定的参数共享模式，自适应地选择正确的参数共享模式，从而提高了学习器的预测精度。进行了广泛的数值研究，以评估所提出的方法的性能。
Rapid developments in data collecting devices and computation platforms produce an emerging number of learners and data modalities in many scientific domains. We consider the setting in which each learner holds a pair of parametric statistical model and a specific data source, with the goal of integrating information across a set of learners to enhance the prediction accuracy of a specific learner. One natural way to integrate information is to build a joint model across a set of learners that shares common parameters of interest. However, the parameter sharing patterns across a set of learners are not known a priori. Misspecifying the parameter sharing patterns and the parametric statistical model for each learner yields a biased estimator and degrades the prediction accuracy of the joint model. In this paper, we propose a novel framework for integrating information across a set of learners that is robust against model misspecification and misspecified parameter sharing patterns. The main crux is to sequentially incorporates additional learners that can enhance the prediction accuracy of an existing joint model based on a user-specified parameter sharing patterns across a set of learners, starting from a model with one learner. Theoretically, we show that the proposed method can data-adaptively select the correct parameter sharing patterns based on a user-specified parameter sharing patterns, and thus enhances the prediction accuracy of a learner. Extensive numerical studies are performed to evaluate the performance of the proposed method.