Automatic Tuning

Automatic tuning aims to optimize the parameters of complex systems, algorithms, or models without manual intervention, improving efficiency and performance. Current research focuses on leveraging machine learning techniques, particularly Bayesian optimization and reinforcement learning, often in conjunction with large language models or pre-trained foundation models, to automate this process across diverse applications. This automated approach is significantly impacting fields ranging from particle accelerator control and robotics to database management and deep learning model adaptation, enabling more efficient and effective utilization of complex systems.