First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper proposes a new regression method, namely calibrated multivariate regression (CMR), for high dimensional data analysis. Besides proposing the CMR formulation, the paper focuses on (1) using a smoothed proximal gradient method to compute CMR's optimal solutions; (2) analyzing CMR' statical properties. One key contribution of the paper lies in the introduction of this CMR formulation; its loss term can be interpreted as calibrating each regression task's loss term with respect to its noise level. I am wondering whether there is any more intuitive interpretation behind the use of the noise level for calibration?

Neural Information Processing Systems http://nips.cc/

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper presents a framework for post model selection inference in the context of marginal screening, which is a computationally efficient way of variable selection in high dimensional linear regression problems. While the paper is focused on marginal screening, the approach is applicable to a broad range of problems. The paper is well written, very clear. I am not an expert in this area, but it seems like a significant problem and a statistically solid approach.

Neural Information Processing Systems http://nips.cc/

We study methods for simultaneous analysis of many noisy experiments in the presence of rich covariate information.

Neural Information Processing Systems http://nips.cc/