Lipschitz Bound

A Lipschitz bound quantifies the maximum change in a function's output given a change in its input, essentially measuring its sensitivity. Current research focuses on leveraging Lipschitz bounds to improve the robustness and generalization of machine learning models, particularly neural networks (including convolutional and transformer architectures) and graph neural networks, often through regularization techniques during training. This is achieved by constraining the Lipschitz constant, leading to improved certified robustness against adversarial attacks and enhanced fairness in model outputs. The resulting advancements have significant implications for the reliability and trustworthiness of machine learning systems across various applications.