Keynote: The 5 W's and H of constrained learning

Abstract: Machine learning (ML) and artificial intelligence (AI) now automate entire systems rather than individual tasks. As such, ML/AI models are no longer responsible for a single top-line metric (e.g., prediction accuracy), but must face a growing set of potentially conflicting system requirements, such as robustness, fairness, safety, and alignment with prior knowledge. These challenges are exacerbated in uncertain, data-driven settings and further complicated by the scale and heterogeneity of modern ML/AI applications that involve from static, discriminative models (e.g., neural network classifiers) to dynamic, generative models (e.g., Langevin diffusions used for sampling). This keynote defines WHAT constitutes a requirement and explains WHY incorporating them into learning is critical. It then shows HOW to do so using constrained learning and illustrates WHEN and WHERE this approach is effective by presenting use cases in ML for science, safe reinforcement learning, and sampling. Ultimately, this talk aims to convince you (WHO) that constrained learning is a key tool to building trustworthy ML/AI systems, enabling a shift from a paradigm of artificial intelligence that is supposed to implicitly emerge from data to one of engineered intelligence that explicitly does what we want.

Short Bio: Luiz F. O. Chamon is an assistant professor (tenure-track) and Hi! PARIS chair holder in the center for applied mathematics (CMAP) of École polytechnique, France. He received the Ph.D. degree in electrical and systems engineering from the University of Pennsylvania (Penn), USA. He received both the best student paper and the best paper awards at IEEE ICASSP 2020. In 2022, he received the Young Investigators award from the Division of Engineering and Applied Sciences, Caltech. In 2025, he received the S.S. Chern Young Faculty Award. He is currently an ELLIS Scholar of the European Laboratory for Learning and Intelligent Systems. His research interests include optimization, signal processing, machine learning, statistics, and control.