Robust Algorithm

Robust algorithms aim to create computational systems that maintain reliable performance despite variations in data, environment, or adversarial attacks. Current research focuses on developing robust versions of existing algorithms (e.g., doubly robust estimators, graph-based semi-supervised learning, and various optimization methods) and exploring their theoretical properties, particularly in high-dimensional settings and under different corruption models (e.g., strong contamination, node corruption). This field is crucial for ensuring the trustworthiness and reliability of machine learning models and other computational systems across diverse and potentially hostile environments, impacting applications ranging from healthcare to security.