Robust PAC

Robust PAC learning aims to develop machine learning models that are resistant to adversarial attacks, where inputs are maliciously perturbed to cause misclassification. Current research focuses on understanding the computational requirements for robust learning, exploring relaxations of worst-case robustness assumptions to improve learnability, and developing algorithms with provable guarantees under various attack models, including those involving semi-supervised learning and ensemble methods. These advancements are crucial for building reliable and secure machine learning systems in real-world applications where adversarial examples pose a significant threat. The development of robust PAC learning algorithms with improved sample complexity and generalization bounds is a key area of ongoing investigation.