Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: 1. Assumption 2.2 in the blog post (normalized linear predictors) isn't really necessary in the paper. What we really need in the proof is just the ratio bound Ew∼ν[ww⊤] / σk2(W*) ≤ O(1/T), which is implied by combining Assumptions 4.3 and 4.4 in the paper. That said, it's helpful to think of each linear predictor as normalized, which is a natural setting under which we can better understand our assumptions, and this is why we stated this in the paper.

2. The blog post lists all 7 assumptions together, and then presents several different results afterward. However, each result only requires some of the assumptions, not all. Although the post does state what assumptions are needed in each result, this wasn't made clear before, which makes the assumption section confusing -- the readers would assume that all the 7 assumptions are required simultaneously. Perhaps it should be made clear before stating the assumptions that not all these assumptions will be required in the results that follow.

3. It would be better if the article can attribute the paper to all the authors, instead of only mentioning it as a paper by Du et al. (The names of other authors never appear even once.) Notice that the authors are alphabetically ordered, and all authors contributed significantly to the paper.

review_title: Authors comments

final_score: rating: 7: Good paper, accept
review_content: After a short motivation, the authors introduce the multi-task representation (MTR) learning. I very appreciated their very high-level (and fun) descriptions and motivations of the assumptions. The the assumptions the authors state the learning bounds in 3 different scenarios and the insights that the bounds provide. After this, they describe Meta-Learning and differences and similarities between it and MTR. As I understand, the punchline is that a modification of the popular MAML and MTR have the same predictions up to some small error. I feel like this blog post is a good overview of many results and it could help many people to learn. At least I learned a few things from reading it.
review_title: A pleasant-to-read overview and summary

Decision: Accept