Extrapolation Framework

Extrapolation frameworks aim to enable machine learning models to accurately predict outcomes for inputs outside the range of their training data, a crucial capability for many real-world applications. Current research focuses on understanding the limitations of existing methods, particularly concerning the axes of difficulty along which extrapolation succeeds or fails, and on developing novel approaches such as multi-fidelity models, constrained optimization techniques, and methods leveraging large language models or diffusion models. These advancements are significant because reliable extrapolation is essential for safe and effective deployment of AI in high-stakes domains like robotics, climate modeling, and scientific discovery, where complete training data is often unavailable.