Review for NeurIPS paper: Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search

Weaknesses: While the general idea of the paper is appealing and has been evaluated extensively, the presentation of the methodology is lacking in clarity at times. After reading section 3, some issues could have been addressed more clearly: • Regarding line 171/172: what do the authors mean by "regret reaches the plateau"? Consider the case of the 1D sine function and we have data points only at increments of pi. K-means would result in two clusters, i.e., the points with values 1 and -1, respectively. What would be the resulting domain for TuRBO then?