Review for NeurIPS paper: Debiased Contrastive Learning

Weaknesses: The main weakness that I see with the paper is the mismatch between the theoretical analysis and the algorithm used in the experiments. The proposed estimator uses samples from the true "positive distribution" which consists of images from the same class. This is of course infeasible in a self-supervised setting where labels are unavailable. As a result, the authors approximate this distribution with the usual "positive distribution" which consists of random transformations of a single image. I understand that this two-step procedure is necessary to have an tractable analysis (using the true "positive distribution") and an experimental approach which is comparable to other self-supervised approaches (which use the approximate "positive distribution"), but the approximation of the "true positive" distribution by the other should be made explicit and discussed.