Meta Learning Bayesian Optimization

Meta-learning Bayesian optimization (BO) aims to improve the efficiency of Bayesian optimization by leveraging experience from previous optimization tasks. Current research focuses on developing robust meta-learning algorithms, often employing neural networks (like Transformers and neural processes) to model surrogate functions or acquisition functions, and incorporating reinforcement learning for improved exploration-exploitation strategies. This approach holds significant promise for accelerating optimization in various fields, particularly where evaluating objective functions is computationally expensive, such as hyperparameter tuning, materials science, and drug discovery. The development of provably robust methods that can handle diverse and potentially unhelpful past experiences is a key area of ongoing investigation.