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: The authors provide an analytical solution for a family of single layer NNs applied to regression problems. Their solution is valid when y is non-positive and when some other constraints are met. 
strengths_and_weaknesses: I'm not particularly familiarized with convex optimization. That's a hard constraint on the quality of my review, so I won't be able to entirely validate your proof or talk about the math of it all. I apologize for that. That being said, I'd like you to take my review as it is: from someone who knows close to nothing of this area and expected to get some basic intuitions from reading the paper.

I believe the main weakness of this tiny paper is that it gives readers no context on why this particular family of NNs and the proof itself are important for the machine learning community. It'd be nice to have some preliminaries for a total beginner in the subject as me, so that we can understand, at least partially, the importance and strength of this submission. 
suggested_changes: - Please add some preliminaries and expected impacts this analytical solution might have to the community if possible. As I mentioned in the strengths/weaknesses section, as I'm not experienced in this topic, the amount of knowledge I got from the proof alone was extremely scarce. 
- Are there any "toy problems" or some more intuitive application you could add to exemplify its use cases and importance?
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: Optimal weights for regression problems using single layer neural networks were shown as [0, 0, . . . , 0, 0]T  under certain conditions. Relevant proofs were shown in this work.
strengths_and_weaknesses: Strengths :
 
*  The derived proofs were crisp, simple and were built on top of existing work 
*  Relevant work from standard text book was cited

Weaknesses

*  Additional empirical evaluations to support the proofs can always enhance this work
*  This work covers a specific case, however any opportunities to generalise this to other settings can enhance the impact
suggested_changes: n/a
review_title: null

Meta-review: metareview: Pros:
- All proofs are included, making the work reproducible.
- The results are correct.
- An interesting (simple) observation about optimization of single layer ReLU networks is obtained.

Cons:
- The derived result has not been placed in context of any related known results, which affects clarity of writing.
- What is the role/significance/implication of y having all non-positive entries?
- The result essentially point out that the global optimum for the single layer model/optimization-problem considered is not very interesting; and this issue only occurs when all labels are strictly negative, which is rare and easily avoidable.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to archive