论文标题
使用辅助结果对高维分类规则的强大而灵活的学习
Robust and flexible learning of a high-dimensional classification rule using auxiliary outcomes
论文作者
论文摘要
相关结果在许多实际问题中很常见。在某些情况下，一个结果特别感兴趣，而另一些结果是辅助。为了利用所有结果共享的信息，传统的多任务学习（MTL）将平均损耗函数最小化，使所有结果平均损失函数最小化，这可能会导致对目标结果的偏见估计，尤其是当MTL模型被误认为时。在这项工作中，基于将估计偏差分解为两种类型，即在空间内和反对空间，我们开发了一种强大的转移学习方法，以估算具有辅助结果的兴趣结果的高维线性决策规则。所提出的方法包括使用所有结果来提高效率的MTL步骤，以及随后仅使用感兴趣的结果进行校准步骤，以纠正两种类型的偏见。我们表明，最终估计器可以达到比仅使用感兴趣的单一结果的估计误差。进行了模拟和实际数据分析，以证明所提出方法的优越性。
Correlated outcomes are common in many practical problems. In some settings, one outcome is of particular interest, and others are auxiliary. To leverage information shared by all the outcomes, traditional multi-task learning (MTL) minimizes an averaged loss function over all the outcomes, which may lead to biased estimation for the target outcome, especially when the MTL model is mis-specified. In this work, based on a decomposition of estimation bias into two types, within-subspace and against-subspace, we develop a robust transfer learning approach to estimating a high-dimensional linear decision rule for the outcome of interest with the presence of auxiliary outcomes. The proposed method includes an MTL step using all outcomes to gain efficiency, and a subsequent calibration step using only the outcome of interest to correct both types of biases. We show that the final estimator can achieve a lower estimation error than the one using only the single outcome of interest. Simulations and real data analysis are conducted to justify the superiority of the proposed method.
