FIND Seminar

Abstract:

The rapid advancements in large AI models have underscored the importance of scaling laws, which demonstrate that model capability increases with larger architectures and richer datasets. However, these advancements pose dual challenges: i) on the learning front, ensuring that in addition to accuracy, new evaluation metrics – such as fairness, safety, and robustness – are met; ii) on the computing front, meeting stringent demands for efficient sensing, communication, and processing. Addressing these challenges necessitates principled methods to handle multiple (possibly competing) performance metrics and resource constraints. In this talk, I will introduce a unified framework to tackle these challenges in learning and computing problems, grounded in bilevel optimization. This framework provides theoretical guarantees on optimality, complexity, as well as algorithmic scalability for large-scale AI models and systems. I will conclude by showcasing applications of bilevel optimization techniques in NextG wireless problems and IBM’s signal processing systems.