First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The authors provide finite sample bounds on the excess risk of these classifiers. When taken to the limit these bounds reproduce the known consistency results for this class. However, they are superior in two ways: 1. They apply in the finite case 2. They apply to a broader set of metric spaces The presentation is very clear and the intuition is well described.

Furthermore, we are interested in the case where the available unlabeled data is partially observed.

It also does not enjoy SNIS's inherent stability and boundedness.

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

For an image, we use a superpixel segmenter, which selects regions on the image. For text, we use the natural correspondence between an input embedding and a word token. Similar notions of input encodings have also been used in [39, 48]. Based on the definition of non-additive statistical interaction (Def. Based on Eqs. 7 - 9 of Lemma 2: φ (I SOC does not assign attributions to general feature sets, only contiguous feature sequences.

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?