Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Needs Clarification (NC): a submission which does not meet the reviewing criteria and needs clarification for its described problem or solution
review_content: summary_of_contributions: This paper studies the effect of different approaches for selecting initial evaluation points of function in the context of Bayesian optimisation. They observe that in the settings they study random selection of initial sampling outperforms other deterministic approaches.
strengths_and_weaknesses: ++


+ Bayesian optimisation is widely used in several applied sciences. Thus methods that optimize its performance are expected to be influential.



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- There were some parts in the text that were unclear and require additional explanation. In particular the previous approach the authors extend needs some brief description, as well as the extensions they propose.
suggested_changes: - Please give the definition of the Hartmann functions you used in your experiments.


- I would suggest to increase the font size in the plots.

- Please explain briefly in the appendix how the method SETUP-BO works. The paper that you cite is non-open access and your method relies heavily on their method.

- Please clarify what is "Mann-Whitney U test P-value of SETUP-BO". 

- Table 1 needs clarification, and in particular what the entries are representing. Please clarify because it seems that I am missing the main point here. If they are p-values it seems to me that all different variants deliver extremely small p-values.

- Can you give some intuitive explanation on why the random sampling performed better than the grid sampling?
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: This paper investigates the efficacy of random initialization and grid initialization strategies  in comparison to traditional initialization strategies. The authors find that random initialization strategies outperform deterministic initialization for the SETUP-BO algorithm. 
strengths_and_weaknesses: Strengths 
-  The paper provides strong empirical results that validate the hypothesis that poor initialization can result in inefficient optimization. Based on the results provided, the randomization strategy does outperform deterministic strategies.
- The experiments are well-designed and can be reproducible based on the provided description, 

Weakness
- The formatting and language of the paper require significant editing. 
- Random initialization is not a novel idea. However, the paper does provide empirical results to support the methodology. 
suggested_changes: - Consider using a more formal tone when writing the paper. For example, the Abstract mentions a "bunch of experiments" which is underspecified. 
- The paper should go through another editing pass, especially around the descriptions of the figures.
review_title: null

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: This study investigates the usefulness of random and grid initialization strategies for a self-tuned Bayesian optimization approach. Author(s) tested their recommended initialization techniques against deterministic initialization to see how they performed. According to their results, random initialization can enhance Bayesian optimization performance.
strengths_and_weaknesses: ## Strengths
- The article demonstrate clarity and simplicity in their work.
- The work presented is reproducible and therefore easy to compare their work with other existing SOTA.
- The submission follows the basic requirements of ICLR code of conduct. 
- The findings demonstrated in the article are justified based on the work shown in the appendix section and source code link provided. 

## Weakness
- The figure quality is not very good and therefore text are hardly visible
- The benchmarks shown in the article are not enough for the kind of work demonstrated by the author. 
suggested_changes: - The better quality of figures and tables would be good. 
- More benchmarks should be compared with to demonstrated the results achieved by your setup. 
- Minor grammatical mistakes can be seen in the test. Correcting them would make test more effective.
review_title: null

Meta-review: metareview: Strengths:
- A key strength of the work is the well-designed and reproducible experiments. 
- The paper has a well-focused and interesting goal of comparing different initialization strategies for Bayesian Optimization w.r.t. their efficiency. 
- The findings are well-organized and clear evidence is provided.

Areas to improve:
- The main limitation is there is scope for improving the writing, and a careful proofread and attempt to improve the presentation along reviewer suggestions is recommended. 
- More experiments/benchmarks would be useful to further substantiate the claims made in the paper.


confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to present