Recently, machine learning becomes important for the cloud computing service. Users of cloud computing can benefit from the sophisticated machine learning models provided by the service. Considering that users can come from different domains with the same problem, an ideal model has to be applicable over multiple domains. In this work, we propose to address this challenge by developing a framework of robust optimization. In lieu of minimizing the empirical risk, we aim to learn a model optimized with an adversarial distribution over multiple domains. Besides the convex model, we analyze the convergence rate of learning a robust non-convex model due to its dominating performance on many real-word applications. Furthermore, we demonstrate that both the robustness of the framework and the convergence rate can be enhanced by introducing appropriate regularizers for the adversarial distribution. The empirical study on real-world fine-grained visual categorization and digits recognition tasks verifies the effectiveness and efficiency of the proposed framework.

研究多个领域学习单一模型的问题，在云计算服务中，由于维护负载而多个领域（即应用/用户）可以共享同一模型。为了解决这一新挑战，我们开发了一个多领域鲁棒优化框架，旨在通过对抗分布来学习模型并同时学习对抗分布。通过对真实世界的细粒度视觉分类和数字识别任务的实证研究，验证了提出框架的效果和效率。

多域鲁棒优化