Resilient Constrained Learning

Resilient constrained learning focuses on developing machine learning models that not only achieve high accuracy but also satisfy various constraints like fairness, robustness, and safety during training. Current research emphasizes adaptive constraint handling, balancing performance gains from relaxing constraints against the cost of that relaxation, often using techniques inspired by ecological resilience. This approach is crucial for deploying reliable and trustworthy AI systems in diverse applications, improving the safety and dependability of autonomous systems and other data-driven technologies.