In this work, we study a new class of risks defined in terms of the location and deviation of the loss distribution, generalizing far beyond classical mean-variance risk functions. The class is easily implemented as a wrapper around any smooth loss, it admits finite-sample stationarity guarantees for stochastic gradient methods, it is straightforward to interpret and adjust, with close links to M-estimators of the loss location, and has a salient effect on the test loss distribution.

翻译：在这项工作中,我们研究一种按损失分布的位置和偏差界定的新的风险类别,这种类别的范围远远超过传统的中差风险功能。 这种类别很容易作为任何平稳损失的包装袋来实施,它承认对随机梯度方法的有限抽样固定性保证,解释和调整是直接的,与损失分布的M-估计点有密切联系,并且对测试损失分布有显著影响。