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: This paper considered domain adaptation. Sepcifcally, We show that pre-training choices and hard negative mining provide consistent improvements to successfully pair contrastive learning and unsupervised domain adaptation
strengths_and_weaknesses: This paper is generally quite clear (I really like figure 1). The presented technique seems correct and reasonably fine. ThusI think it is a good paper. 

I do not have significant concerns. Due to the page limit, some parts still seem unclear. This could be addressed in the final version.




suggested_changes: I would suggest reporting statistical test and a variance in Table 1.
review_title: null

Meta-review: metareview: Clear paper with solid results.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to present