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: The authors present symbolic regression dataset to be used for recovering a set of financial equations. 
strengths_and_weaknesses: Strengths
- The application of symbolic regress to financial systems is interesting and the paper addresses an important need to develop datasets in this space.


Challenges 
- The methods section requires more details and explanation. It's unclear where the data is coming from. Is it synthetic data? Or is it data from the market activity? The selection criteria for the equations also seem arbitrary. It would be helpful a clear definition of "suitability" for SR.  
- The use of proprietary software for the genetic programming experiments makes it difficult to reproduce the results claimed by the paper.
- The paper underspecifies the details of the transformer models used for the deep learning experiments.
- The paper has a considerable amount of typos and grammatical mistakes. 

suggested_changes: The authors should consider
- adding more details in the methods section concerning the challenges described above.
- there are several typos and grammatical mistakes which should be edited
- using publicly available tools for genetic programming
review_title: null

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: The authors present a new dataset in the field of recovering models from data and symbolic regression (SR). Their main contribution is that they extend SR with equations describing financial systems except for the most dominant physical ones that have already been studied.
strengths_and_weaknesses: The authors select 14 new equations, suitable for SR, that are non-linear and meaningful for financial markets. They evaluate the performance of the equations using two representative SR methods, namely genetic programming and transformer-based neural network. They mention the capability of both models in recovering each non-linear equation. The methods combined recover 9 out of 14 equations. 

Despite the interesting direction, the authors propose in the field of SR and the proposed experiments, the paper lacks important details in terms of reproducibility (choice of equations, hyperparameters, data generation and training, evaluation metrics). Furthermore, regarding the discussion of the results, the analysis and the importance of their findings are rather limited.

suggested_changes: Important questions to be answered: 
Why SR models can recover some of the proposed equations but struggle in some of the tasks?
Which equations could achieve stronger performance in both physical and non-physical systems?
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: This work presents a novel application of symbolic regressions in non-physical systems, specifically in financial economics. The authors provide a ground-truth database that complements existing benchmarks and extend the scope of symbolic regression from physical equations into other domains. The joint performance of deep learning and genetic programming symbolic regression systems is tested in recovering these non-physical equations, with promising results. 
strengths_and_weaknesses: Strengths:
1. The authors provide a novel application of symbolic regressions in non-physical systems, which extends the scope of symbolic regression from physical equations into other domains.
2. The authors provide a ground-truth database that complements existing benchmarks and includes unique variations that algorithms have not seen before.

Weaknesses:
1. The paper only tests the joint performance of deep learning and genetic programming symbolic regression systems in recovering non-physical equations in financial economics, so the generalizability of the results to other non-physical systems is unclear.
2. The paper does not compare the performance of symbolic regression methods with other machine learning approaches for modeling non-physical systems, which limits the assessment of the effectiveness of symbolic regressions in this context.
suggested_changes: 1. The paper could benefit from a more detailed discussion of the limitations and assumptions of symbolic regression methods in modeling non-physical systems. This would help readers better understand the scope and applicability of the proposed approach.
2. The paper could also include a comparison of the performance of symbolic regression methods with other machine learning approaches for modeling non-physical systems, such as neural networks or decision trees. This would provide a more comprehensive assessment of the effectiveness of symbolic regressions in this context and help identify areas for future research.
review_title: null

Decision: Invite to archive