Review for NeurIPS paper: On the Theory of Transfer Learning: The Importance of Task Diversity

Maybe the author can discuss what happens with moderate model misspecification. The theory does not explain why transfer learning works when training tasks are not diverse. In all three examples, the'classifier head' hypothesis class F is linear. I wonder what task-diversity constants (definition 3) can be derived for more complex family F such as a multi-layer neural network. How about logistic loss, or classification? 5. Question: Can more refined bounds than [1] be applied to deep neural networks?