论文标题
使用L $ _2 $标准的稳健结构回归的用户友好型计算框架
A User-Friendly Computational Framework for Robust Structured Regression with the L$_2$ Criterion
论文作者
论文摘要
我们引入了一个用户友好的计算框架，用于用L $ _ {2} $ Criterion实现多种结构化回归方法的强大版本。除了引入用于执行L $ _ {2} $ E回归的算法外，我们的框架还可以通过L $ _ {2} $标准进行其他结构约束，还可以在不需要复杂的调整过程上进行精确的调整过程，可用于识别杂志的亚种不可行，并可以添加构造的构造。我们为该框架提供融合保证，并通过一些示例来证明其灵活性。本文的补充材料可在线获得。
We introduce a user-friendly computational framework for implementing robust versions of a wide variety of structured regression methods with the L$_{2}$ criterion. In addition to introducing an algorithm for performing L$_{2}$E regression, our framework enables robust regression with the L$_{2}$ criterion for additional structural constraints, works without requiring complex tuning procedures on the precision parameter, can be used to identify heterogeneous subpopulations, and can incorporate readily available non-robust structured regression solvers. We provide convergence guarantees for the framework and demonstrate its flexibility with some examples. Supplementary materials for this article are available online.
