Given the theoretical insights provided in this work, the ATTA framework can be applied to other machine learning tasks beyond classification, such as regression or structured prediction problems, by adapting the framework to suit the specific requirements of these tasks. For regression tasks, the ATTA framework can be modified to optimize regression loss functions instead of classification loss functions, allowing the model to adapt to continuous target variables. Techniques like active learning with regression uncertainty can be employed to select informative data points for adaptation and improve the model's performance on regression tasks. In structured prediction problems, the ATTA framework can be extended to handle sequential or structured data by incorporating specialized loss functions and adaptation strategies tailored to the task's specific structure. By customizing the ATTA framework to different machine learning tasks, it can effectively address distribution shifts and improve model performance in a wide range of applications beyond classification.