2 papers
stat.ML2025
Sample-efficient Learning of Concepts with Theoretical Guarantees: from Data to Concepts without Interventions
Hidde Fokkema, Tim van Erven, Sara Magliacane
Machine learning is a vital part of many real-world systems, but several concerns remain about the lack of interpretability, explainability and robustness of black-box AI systems.…
cs.AI2020
Verifiably Safe Exploration for End-to-End Reinforcement Learning
Nathan Hunt, Nathan Fulton, Sara Magliacane +3
Deploying deep reinforcement learning in safety-critical settings requires developing algorithms that obey hard constraints during exploration. This paper contributes a first appro…