3 accepted papers
Bayesian Optimization (BO) is a widely-used method for optimizing expensive-to-evaluate black-box functions. Traditional BO assumes that the learner has full control over all query variables without additional constraints. However, in many real-world scenarios, controlling certain query variables ma…
Optimizing expensive black-box functions under unknown constraints is a fundamental challenge across a range of real-world domains, such as hyperparameter tuning in machine learning, safe control in robotics, and material or drug discovery. In these settings, each function evaluation may be costly o
Bayesian optimization (BO) is a leading method for optimizing expensive black-box optimization and has been successfully applied across various scenarios. However, BO suffers from the curse of dimensionality, making it challenging to scale to high-dimensional problems. Existing work has adopted a va…