Adaptive Algorithm

Adaptive algorithms dynamically adjust their parameters during operation, aiming to optimize performance in environments with changing conditions or unknown characteristics. Current research focuses on improving convergence rates and reducing computational complexity in various applications, including federated learning (using algorithms like FedCAda and FedLion), online learning (with algorithms addressing issues like prior misspecification and switching costs), and bandit problems (exploring both adaptive and non-adaptive strategies for best arm identification and thresholding). These advancements have significant implications for machine learning, control systems, and other fields requiring efficient and robust solutions in dynamic or uncertain settings.