Reviews: Model Similarity Mitigates Test Set Overuse

This paper is concerned with an observation about adaptive data analysis. It relies on a study that shows that despite statistical lower bounds, common practices of adaptive data analysis do not result in overfitting. The authors show that empirically this is a result of the models used in Kaggle competitions behaving in a similar manner. In addition, the authors give a simple model and analyze the model. The reviewers thought this is an interesting direction and that the results were generally well executed.