Originality: I really liked the idea of modeling MAML-like approaches using nonparametric Bayesian priors and I am not familiar with other work that does that. Thus, I consider the proposed method novel. More specifically, I consider it a novel combination of existing methods, that exploits an interesting connection between them. Quality: The paper is of good quality both in terms of key contributions and in terms of how the contributions. are presented. Clarity: The paper is well-written and organized, and very easy to follow, given a bit of background in Bayesian methods. Significance: As mentioned in my earlier comments, I consider this work significant. One other comment is that I really like the extensive discussion of related work, both in the paper and in the supplementary material. I always find that very useful and often ignored so I was positively surprised in your case.

Overall, this is a very strong submission. It is well-written, timely, clear, and includes several significant and novel contributions. The technical contributions are well-developed, and it does a very nice job addressing a challenging problem in meta-learning. Although the novelty is high, it is related to several papers on online MAML and MAML with task-clustering that appeared in ICML 2019. Although I know the timing is very tight between these, so it isn't reasonable to expect empirical comparison, those works should at least be cited and compared qualitatively in related work. I would like to see more in the paper addressing the tension between the ability to fit the meta-parameters to each task clusters and the ability to generalize. This has a nice interpretation in the Bayesian setting that could be called out further. The clarity of Section 3 would be improved by adding a plate diagram or illustrative figure showing the relationship among the variables. POST-RESPONSE Authors -- thanks for your clear and engaging response. Please do add the plate diagram back in, at least as supplemental material, but preferably in the main paper.

The problem is interesting. The combination of continual learning and meta-learning is novel. The method is technically sound. The experiments on toy tasks are well-designed. The paper is clearly written. ======= UPDATE ======= I have read the authors' response and understood the difficulty of finding a naturalistic dataset.