Bei Liang

Automated Random Embedding for Practical Bayesian Optimization with Unknown Effective Dimension

Bayesian optimization is widely employed for optimizing complex black-box functions but struggles with the curse of dimensionality. Random embedding, as a dimension reduction strategy, simplifies tasks that possess the effective dimension by optimizing within a low-dimensional subspace. However, det