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: Anchor optimization with limited seeds(limited parallel anchors) for relative representation: unfortunately no reproducible code provided
strengths_and_weaknesses: 
Strengths
1. Author introduces a Novel method to reduce no of parallel anchor for relative representation
2. Commendable to do both Qualitative & quantitative measures of evaluation
3. author suggests this approach(Anchor optimization) has competitive performance in NLP & CV domains
4. based on this paper, Anchor Optimization outperforms Ground Truth parallel anchors
5. Author has done a great job explaining algorithmic approach provided in appendix
6. Anchor Optimization hyperparameters in retrieval & zero-shot stitching tasks information provided in appendix
7. author has also provided the tools & technologies utilized in their approach (PyTorch Lightning, GeoTorch, Hugging face transformers & datasets, DVC,Fast, Memory-Efficient Approximate Wasserstein Distances)
Weakness
1. The author has done thorough research on this paper, explained the algorithmic approach in appendix, but failed to provide code to make these results reproducible.

suggested_changes: 1. Do attempt to provide reproducible code, the evaluation criteria is highly based on this.
2. Though the authors have done great research easily making this top 20% of the tiny papers, unable to provide higher rating due to non-availability of reproducible code.
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 proposes a method for aligning/stitching the latent space of different domains using a small set of parallel anchors (called seeds). The approach involves alternately optimizing the latent permutation and aligned projection. The proposed method is effective and reduces the number of seeds needed for stitching different domains.
strengths_and_weaknesses: ### Strengths 

1. Good research question. The problem of aligning the latent space of pretrained models using a few parallel samples is interesting and has potential applications in different domains. Reducing the parallel data points required for the alignment is thus important.

2. Effective method. The proposed method is backed up by quantitative results and visualizations that demonstrate its effectiveness.

3. Straightforward algorithm. The algorithm is designed without ad-hoc components, making it easy to follow.

### Weaknesses

1. Writing clarity. The writing could be polished to improve the clarity of the paper, especially considering that Tiny Papers should have a broader audience. The authors are encouraged to provide a better explanation of their methods in the main paper.
suggested_changes: 1. Add a table of notions to help readers understand the terminologies used in the paper.

2. Expand on the explanation of the authors' ideas and methods in the main paper, including their motivation and design, while moving some of the experiments to the appendix.
review_title: null

Decision: Invite to present