Limited Information

Limited information scenarios, where data is incomplete, noisy, or scarce, pose significant challenges across diverse scientific fields. Current research focuses on developing robust methods and algorithms, including Bayesian optimization, deep learning architectures (like convolutional and graph neural networks), and large language models, to effectively extract knowledge and make accurate predictions despite these limitations. This work is crucial for advancing applications ranging from human mobility modeling and medical AI to robotic control and cybersecurity, where access to complete data is often impractical or impossible. The overarching goal is to create reliable and efficient systems that can function effectively even with incomplete or imperfect information.