Online Learner

Online learning focuses on developing algorithms that learn and adapt continuously from sequentially arriving data, aiming to minimize errors and optimize performance over time. Current research emphasizes efficient model architectures like state-space models, which offer improved scalability compared to transformers, and explores optimal strategies for handling irreversible decisions and limited data in various applications. This field is crucial for addressing challenges in areas such as massive online course grading, robust federated learning in dynamic networks, and improving the security and reliability of machine learning models against adversarial attacks.