1. Although the authors present the exact formulation of the risk in the main text, it is complicated to understand the implications of those formulas. It would be helpful to include more discussion to explain each term to better understand the results.
2. The paper's main contribution is to examine the bias amplification phenomenon using the formula. However, a formal statement about how different components affect the bias amplification is lacking. I would suggest the authors write them in formal theorems.
3. It is unclear how these theoretical findings relate to real-world deep learning models, I would suggest the authors verify the conclusion about the label noise and model size on MNIST and CNN as well.
3. It is unclear how these theoretical findings relate to real-world deep learning models, I would suggest the authors verify the conclusion about the label noise and model size on MNIST and CNN as well.