A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution. However, this assumption is violated in almost all practical applications: machine learning systems are regularly tested on data that are structurally different from the training set, either due to temporal correlations, particular end users, or other factors. In this work, we consider the setting where test examples are not drawn from the training distribution. Prior work has approached this problem by attempting to be robust to all possible test time distributions, which may degrade average performance, or by "peeking" at the test examples during training, which is not always feasible. In contrast, we propose to learn models that are adaptable, such that they can adapt to distribution shift at test time using a batch of unlabeled test data points. We acquire such models by learning to adapt to training batches sampled according to different sub-distributions, which simulate structural distribution shifts that may occur at test time. We introduce the problem of adaptive risk minimization (ARM), a formalization of this setting that lends itself to meta-learning methods. Compared to a variety of methods under the paradigms of empirical risk minimization and robust optimization, our approach provides substantial empirical gains on image classification problems in the presence of distribution shift.

本文探讨了在机器学习系统被分布转移影响时, 如何通过自适应风险最小化方法 (ARM) 以提高对新领域和分布的分类准确率, 在多个图像分类问题中, 与其他鲁棒性、不变性和适应性方法相比，ARM方法提高了1-4％的测试准确率。

自适应风险最小化：学习适应领域转移