Automated Random Embedding for Practical Bayesian Optimization with Unknown Effective Dimension 事件

Automated Random Embedding for Practical Bayesian Optimization with Unknown Effective Dimension arXiv:2605.23473v2 Announce Type: replace-cross Abstract: 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, determining the effective dimension of a task in