Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The authors in this work examines the problem of quota-constraint in subset selection problem. They analyse this for two popular scenarios: Submodular Maximisation and Sampling from Detrimental Point Process
strengths_and_weaknesses: .Strengths:
* The paper clearly introduces the problem and clearly specifies what they want to solve in this paper.

Weakness:
* The major portion of section 2 including algorithm and ablations is available in Appendix, this breaks the flow for the reader.
suggested_changes: * Try to  minimise introduction and bring algorithm to the main paper rather than in the Appendix
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: The paper analyzes quota constraints for maximizing monotone submodular function and sampling from a determinantal point process and proposed QuotaGreedy and CapacityGreedy algorithms. The proposed algorithms improve group-wise diversity while not damaging the utility of the constructed synthetic data.
strengths_and_weaknesses: Strengths:
1. The paper is thorough, clear and very well-written. 
2. The proposed algorithms seem promising.

Weaknesses:
I don't see any, but I am not very familiar with the studied field.
suggested_changes: Not really, as I don't research in this field.
review_title: null

Meta-review: metareview: Pros:
- The authors study an interesting problem.
- The proposed algorithms for ensuring "diversity/quota constraints" make sense.

Cons:
- The theoretical results are not very clear (See detailed feedback).
- Code for experiments is not provided.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to archive