On Continuity of Robust and Accurate Classifiers

The reliability of a learning model is key to the successful deployment of machine learning in various applications. Creating a robust model, particularly one unaffected by adversarial attacks, requires a comprehensive understanding of the adversarial examples phenomenon. However, it is difficult to describe the phenomenon due to the complicated nature of the problems in machine learning. It has been shown that adversarial training can improve the robustness of the hypothesis. However, this improvement usually comes at the cost of decreased performance on natural samples. Hence, it has been suggested that robustness and accuracy of a hypothesis are at odds with each other. In this article, we put forth the alternative proposal that it is the continuity of a hypothesis that is incompatible with its robustness and accuracy in many of these scenarios. In other words, a continuous function cannot effectively learn the optimal robust hypothesis. We introduce a framework for a rigorous study of harmonic and holomorphic hypotheses in learning theory terms and provide empirical evidence that continuous hypotheses do not perform as well as discontinuous hypotheses in some common machine learning tasks. From a practical point of view, our results suggest that a robust and accurate learning rule would train different continuous hypotheses for different regions of the domain. From a theoretical perspective, our analysis explains the adversarial examples phenomenon in these situations as a conflict between the continuity of a sequence of functions and its uniform convergence to a discontinuous function. Given that many of the contemporary machine learning models are continuous functions, it is important to theoretically study the continuity of robust and accurate classifiers as it is consequential in their construction, analysis, and evaluation. It is important in their construction as discontinuities can lead to artifacts in their approximations. It is necessary to analyze these classifiers as they carry topological information about their domain. It is critical in their evaluation because it reveals the ways that performance metrics, such as the accuracy score, can fail in assessing these classifiers.