There is a growing need for models that are interpretable and have reduced energy and computational cost (e.g., in health care analytics and federated learning). Examples of algorithms to train such models include logistic regression and boosting. However, one challenge facing these algorithms is that they provably suffer from label noise; this has been attributed to the joint interaction between oft-used convex loss functions and simpler hypothesis classes, resulting in too much emphasis being placed on outliers. In this work, we use the margin-based $\alpha$-loss, which continuously tunes between canonical convex and quasi-convex losses, to robustly train simple models. We show that the $\alpha$ hyperparameter smoothly introduces non-convexity and offers the benefit of "giving up" on noisy training examples. We also provide results on the Long-Servedio dataset for boosting and a COVID-19 survey dataset for logistic regression, highlighting the efficacy of our approach across multiple relevant domains.

翻译：越来越需要可解释的模型,这种模型减少了能量和计算成本(例如,在保健分析学和联合学习中)。培训这些模型的算法实例包括后勤回归和提升。然而,这些算法所面临的一个挑战是,这些算法可能受到标签噪音的影响;这可归因于经常使用的锥形损失功能和较简单的假设类别之间的联合互动,导致过分强调外层。在这项工作中,我们使用以差值为基础的美元(alpha$)损失,它不断调控卡通性锥体和准螺旋损失,以强有力地训练简单模型。我们表明,美元超正数的超正值平稳地引入非共性,并提供了“原谅”噪音培训实例的收益。我们还提供了长期服务数据集的促进结果和用于物流回归的COVID-19调查数据集,突出了我们跨多个相关领域的方法的功效。