Robust Linear

Robust linear methods aim to develop machine learning models and algorithms that are resilient to noise, outliers, and adversarial attacks while maintaining accuracy and interpretability. Current research focuses on improving robustness in various model architectures, including linear parameter-varying state-space models and implicit neural networks, often employing techniques like graduated nonconvexity and adaptive loss functions to achieve this. These advancements are crucial for deploying reliable machine learning systems in safety-critical applications, particularly where explainability and recourse mechanisms are essential, as highlighted by the trade-offs observed between robustness and actionable explanations. The ultimate goal is to create robust and trustworthy AI systems across diverse domains.