In this paper, we explore a new approach to improve machine learning models’ ability to generalize to unseen domains through adversarial data augmentation. We introduce the concept of distributionally robust optimization, which enables us to design algorithms that can adapt to different distributions in an optimal manner. By leveraging this framework, we develop strategies that can minimize the simple regret or error probability incurred when adapting to new environments.
Our proposed methods are based on non-adaptive exploration strategies that aim to balance the trade-off between exploitation and exploration. We demonstrate through theoretical analysis and simulations that our approach leads to better performance compared to existing methods, particularly in scenarios with distribution shifts.
To illustrate the practical applicability of our findings, we provide examples of real-world applications where adversarial data augmentation can be employed. These include image classification tasks, natural language processing, and recommendation systems. By adopting a robust learning framework, these models can better adapt to new environments and improve their overall performance.
In summary, this paper presents a novel approach to enhance the generalization capacity of machine learning models by leveraging adversarial data augmentation techniques. Our proposed methods provide a robust foundation for adapting to unseen domains, leading to improved performance in real-world applications. By understanding the principles of distributionally robust optimization and the strategies for minimizing simple regret or error probability, practitioners can now develop more effective machine learning models that can tackle complex problems with greater accuracy.
Distributionally Robust Learning: A Comprehensive Survey
