论文标题
用于基于超品质的监督学习的一阶优化
First-order Optimization for Superquantile-based Supervised Learning
论文作者
论文摘要
通过经验风险（或负模样）的经典监督学习最小化的假设是，测试分布与训练分布一致。在机器学习的现代应用中，可以对此假设进行质疑，在该应用程序的现代应用中，学习机可以在预测时间内使用分布与培训数据之一相关的测试数据。我们通过提出一阶优化算法来最大程度地减少基于超品质的学习目标，从而重新审视超品质回归方法。所提出的算法基于通过虚拟卷积平滑超品质功能。有希望的数值结果说明了采取更安全的监督学习方法的兴趣。
Classical supervised learning via empirical risk (or negative log-likelihood) minimization hinges upon the assumption that the testing distribution coincides with the training distribution. This assumption can be challenged in modern applications of machine learning in which learning machines may operate at prediction time with testing data whose distribution departs from the one of the training data. We revisit the superquantile regression method by proposing a first-order optimization algorithm to minimize a superquantile-based learning objective. The proposed algorithm is based on smoothing the superquantile function by infimal convolution. Promising numerical results illustrate the interest of the approach towards safer supervised learning.
