Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: Conformal prediction in practice
strengths_and_weaknesses: This paper discussed conformal prediction applied to neural network classifications when high coverage is required for safety reasons. The paper analysed both theoretically and empirically about this question.

It is am important question to be studied in order for conformal prediction to be meaningfully applied to DL models.

The idea is clear but I have a question regarding the theoretical result which needs clarifications, see below.

Also some typos can be found, e.g.:

"...any CP method (e.g. APS or RAPS) will **short** the output logits..": short -> sort

I suggest a careful read through again to check the typos.
suggested_changes: The way conformal prediction sorts the logits depends on the scoring function. I suggest you to clarify this, e.g., by saying the scoring function is monotonically non-decreasing in the logit values.
review_title: null

Decision: Invite to present