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 paper proposes a loss modification approach to improve generalization performance in neural networks.
strengths_and_weaknesses: The authors provide a sound theoretical framework for their approach. However, the paper could benefit from an empirical evaluation of the proposed approach to demonstrate its effectiveness. Additionally, the paper does not include a description of the data used in the experiments, making it difficult to replicate the results.
suggested_changes: - The authors should consider rephrasing the abstract and introduction to better highlight the main contribution of the paper.
- The paper would benefit from a more detailed description of the experiments, including the data sets used, the choice of hyperparameters, and the evaluation metrics used to demonstrate the effectiveness of the proposed approach.
review_title: null

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: This paper proposes a simple loss modification to improve generalization in neural networks by considering the properties of the data set distribution rather than specific data samples. 
strengths_and_weaknesses: Strengths
1. This paper is well written, motivation is clear and I enjoyed reading it. 
2. The loss modification method is simple and can be easily implemented. 

Weakness
1. No experimental Results
2. Not connected well to existing literature. 
suggested_changes: Suggested Changes:
1. Incorporate atleast 1 set of experiments to show the validity of your method. 
2 Connect with the existing literature.
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 paper presents an important topic in generalization of model performance by 'differential' parameter optimization. Parameters of a neural network are learned by averaging its distribution over all possible subsets of the training set.
strengths_and_weaknesses: 1. The topic dealt with in the paper is of utmost significance :- improving the generalization power of neural networks. 
2. The new loss function that has been proposed exhibits insensitivity to high-loss data points. Thus, the model is provably robust to outliers and hence generalizes well.
3. The structure of the paper is mostly monolithic. It lacks in a specific section for concluding remarks and future work as well as some insights on the experiments performed and comparison study with state-of-the-art learning techniques.
suggested_changes: I thought that the paper highlights an important area in learning theory of neural nets, however providing a structure to the paper by adding sections would be nice. It would also help in reproducing the results of the paper if some insights on experimental results is provided.
review_title: null

Decision: Invite to revise