Paper:
Testing and Learning on Distributions with Symmetric Noise Invariance
Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD), the resulting distance between distributions, are useful tools for fully nonparametric two-sample testing and learning on distributions. However, it is rare that all possible differences between samples are of interest -discovered differences can be due to different types of measurement noise, data collection artefacts or other irrelevant sources of variability. We propose distances between distributions which encode invariance to additive symmetric noise, aimed at testing whether the assumed true underlying processes differ. Moreover, we construct invariant features of distributions, leading to learning algorithms robust to the impairment of the input distributions with symmetric additive noise.

Review:
Overview: In this paper the authors present a method for making nonparametric methods invariant to noise from positive definite distributions by utilizing estimated characteristic functions while disregarding magnitude and only considering the phase. The authors apply the technique to two sample testing and nonparametric regression on measures, ie each "sample" is a collection of samples. The authors demonstrate that the techniques introduced behave as expected by performing tests on synthetic data and demonstrate reasonable performance of the new two sample tests and new feature on real and semi-synthetic datasets.
Theory: The fundamental idea behind this paper (disregarding the magnitude of a characteristic function) is not terribly revolutionary, but it is elegant, interesting, and likely to be useful so I think worthy of publication. Though the underlying concept is somewhat technical, the authors manage to keep the exposition clear. The authors explain the concept using population version of estimators and give what seem to be very reasonable finite sample estimators, but it would be nice to see some sort of consistency type result to round things off.
Experiments: The synthetic experiment clearly demonstrates that the new features behave as one might expect in for a two sample test, but it is disappointing that the technique seems to greatly reduce the test power compared to current nonparametric techniques. This difference is pretty striking in the >=2000 sample regime. While the technique is not effective for the Higgs Boson two sample test its ineffectiveness is in itself very intriguing, the difference between the two classes seems to be some sort of psd noise phenomena. For the last two experiments, the proposed technique works well on the semi-synthetic dataset (real data with synthetic noise added), but not particularly better, on the final real world data experiment. Ultimately the proposed technique doesn't demonstrate decidedly superior testing or regression performance on any real world dataset, although it does slightly outperform in certain sample regimes.
Exposition: The paper is a pleasure to read but there are some errors and problems which I will list at the end of this review. 
Final Eval.: Overall I think the idea is interesting enough to be worthy of publication.
--Corrections By Line--
3: "rarely" should be "rare"
14: "recently" should be "which have recently been" to sound better
53: To be totally mathematically precise perhaps you should state the E is a psd rv again
115: add "the" after "However"
116: I'm assuming that "characteristic function" was meant to be included after "supported everywhere"
119: Should probably mention a bit more or justify why these "cases appear contrived"
123: The notation P_X and P_Y hasn't been introduced, but perhaps it is obvious enough
123: Here K is a function on a pair of probability measures on line 88 K is a function of two collections of data
134: We need some justification that this is actually an unbiased estimator. A lot of the obvious estimators in the MMD and related techniques literature are not unbiased
208: \chi^2(4)/4 is not technically correct, its the sample that is divided by 4 not the measure
268: I'm not familiar with this dataset. Please mention the space which the labels lie in. I'm assuming it is R.
Update: I've bumped it up a point after reading the other reviews and rebuttal. Perhaps I was too harsh to begin with.