Tutorials

Requirements are inherent to systems. This is because systems are always defined as tradeoffs between multiple competing specifications such as, e.g., stability, safety, robustness, and efficiency. Learning to satisfy requirements is, however, antithetical to the standard ML practice of minimizing individual losses. To close this gap, we develop the theory and practice of constrained learning. This tutorial provides an overview theoretical and algorithmic developments that show when and how it is possible to learn with constraints. We describe how theoretical guarantees and viable learning algorithms are hindered by lack of convexity of the resulting optimization problems and explain how a near-duality theory circumvents this challenge. Throughout the tutorial we explore supervised learning, robust learning, and reinforcement learning with constraints. We put emphasis on showcasing the breadth of potential applications by discussing fairness, robust classification, federated learning, learning under invariance, and safe reinforcement learning. Attendees will be prepared to start conducting research in this emerging research frontier.