NeurIPS 2020

Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization

Meta Review

The paper has been actively discussed after the rebuttal that the reviewers found useful and actionable (e.g., about the practical usage of the method---choice of its hyperparameters and overall competitiveness---and about the novelty/Incremental aspects of the submission). The paper is recommended for acceptance. All reviewers have acknowledged that the paper makes a step towards better understanding BO in higher-dimensional problems. The paper (with ALEBO) should also set a much better baseline than REMBO for future research. As promised in the rebuttal, it is important to include in the final version of the paper the new elements such as (i) a discussion about the flexibility and (ii) results for TurBO/CMA-ES.