General Algorithm

General algorithms aim to create adaptable solutions applicable across diverse problem domains, avoiding the need for task-specific designs. Current research focuses on improving efficiency and optimality, encompassing areas like online prediction, adversarial example generation, and hyperparameter optimization in federated learning. These advancements are significant because they offer more robust and efficient solutions for a wide range of applications, from data preprocessing and knowledge graph embedding to machine learning model training and optimization. The development of universal algorithms with provable guarantees is a key driver of progress in this field.