Review for NeurIPS paper: Functional Regularization for Representation Learning: A Unified Theoretical Perspective

Summary and Contributions: Post-rebuttal comments Thank you for the response. I am happy with the explanations and will increase my score, thus recommending the paper for acceptance. The paper provides a theoretical background for learning tasks that combine two steps: i) representation learning (e.g., via auto-encoders or self-supervised learning) and ii) supervised learning with instances represented via the features learned in step i). The assumption is that in addition to labelled examples the algorithm has access to unlabelled instances. The first step learns a representation function h(x) that belongs to some hypothesis space H.