Reviews: Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration

After a careful discussion among the reviewers, there is a clear consensus that the paper provides a solid contribution to the community. As a result, I would recommend acceptance for publication at NeurIPS2019. One important concern that came up during the discussion is that it is unclear under which regime the paper is focusing on. As a result, it becomes difficult for the reviewers and readers to assess the actual contribution. For example, the authors need to clarify that the paper needs \beta \geq 1/2 to hold and that it considers *only* the case \alpha 1 .