Review for NeurIPS paper: Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization

Additional Feedback: I think this is a good paper that will inform future work on high-dimensional BO. Having highlighted a number of severe shortcomings of linear embeddings, I expect future work to either leverage the insights of ALEBO to develop a truly competitive baseline, or simply use these lessons learned to focus on different methods, such as the model-free ones. The robot locomotion experiment does suggest that linear embeddings, despite all improvements, are still not suited to be the default for high dimensional BO. Not only are they outperformed by model-free methods, such as CMA-ES, but also by some model-based ones such as TuRBO (despite the larger variance, as shown in the appendix). In any case, while we do not have a new state of the art method for high-dimensional BO out of this paper, the contribution is useful and will inform future work in this space.