First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper presents a novel approach for column sampling when the data point clusters comprise of non-convex hulls. Column sampling is important in selecting a small subset of data that represents the properties of the original dataset. The presented approach is based upon the computation of Zeta hulls. The authors model the graph cycles by means of the sum-product rule and integrate them using the Zeta function. The authors set up the optimization problem as finding the subset of points with the strongest point extremenesses.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper combines evidence-based clustering with an importance sampling approach. It further optimizes the weight function calculation of importance sampling, by using a second sampler to estimate the number of true groundings. I recommend to accept this paper because of the importance of the evidence problem, and the fact that the proposed solution is very general and scalable. The paper is well-written and clear, except for Figure 1 (b) and Equations 5,6, and 7, which are poorly explained.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. An attentional mechanism is added to a feedforward convolutional network, with recurrence introduced by the feedback of succeeding attentional focus locations. The system is trained using RL, due to issues of differentiability making the performance gradient unavailable. The system achieves competitive performance, with a dramatic reduction in computational burden. This work tackles an important problem, and makes good progress.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper considers weighted majority algorithm and establishes consistency (error rate of the aggregator tending to zero) results under two settings: (1) when the competence level (risk of each expert) is known in advance and (2) when it is estimated. For case (2), frequentist and Bayesian methods for estimating the competence level are provided. For case (1), consistency is established in terms of providing upper and lower bounds on the error rate of the aggregator, which involve standard calculations ( apart from the fact that upper bound is established by invoking a result by Kearns and Saul, instead of Hoeffding's inequality). For case (2) under the frequentist setting, an independent set of labeled inputs is used to estimate the competence level of each expert.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Paper ID: 39 Title: Robust Classification Under Sample Selection Bias NOTE: Due to the short reviewing time and out of fairness to the other papers I was reviewing, I DID NOT do more than glance over the supplementary material. In most cases, I therefore did not verify that results claimed by the authors are correct, but instead checked that they are plausible. Summary: This paper is about how to adjust a classifier when the training set is not representative of the test set; a canonical example is active learning, but the problem can also appear in recalibration. The goal is to find an effective method in the finite-sample regime, since most results are asymptotics.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper addresses the issue of object detection, in particular the challenge of obtaining bounding boxes on a scale similar to which category labels exist for object categorization. The authors side-step this challenge by proposing to adapt object classifiers for the detection task. Their algorithm is fairly simple and straightforward, which is not a bad thing in itself. Their experimental protocol uses 100 categories for training (with both category labels and bounding boxes), and tests on 100 left-out categories.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper studies the statistical consistency of plug in classifiers under non decomposable loss functions such as the F statistic which is a popular performance measure in machine learning. The problem studied in this paper is complex because non decomposable measures cannot, by definition, be expressed as an empirical expectation. Therefore, usual concentration inequalities are not applicable in this scenario. The authors present a general analysis for measures that can be expressed as a continuous function of the true positive rate and the true negative rate as well as the class probability.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper studies the worst case hardness of estimating the parameters of a binary pairwise undirected graphical model. By considering the specific case of the hard-core / independent set model and relying on the known result that approximating the partition function for this problem given the parameters (even for the unweighted case of all theta_i being 0) is hard, the authors show that the other direction -- namely, approximating the parameters given the node marginals -- is hard, in the sense that it does not admit an FPRAS. This is a strong theoretical paper, addressing a computational complexity problem that occurs very often in theory and practice, has been conjectured to be hard, but hadn't yet been shown formally to be hard (to the best of my and the authors' knowledge). The work is unlikely to have much impact in practice.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper proposes a denoising algorithm based on non-local image statistics and patch repetition by combining the advantages of NL-means and Exponentially Weighted Aggregation (EWA). The computation of the aggregated estimator is done using MCMC and results are comparable to state-of-the-art algorithms. Pluses: 1) the method seems simple and straightforward to implement. Minus: In terms of explaining *why* the method works, the text leaves something to be desired. E.g., in the second paragraph of section 7 (The proposed implementation proceeds in two identical iterations.)

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper presents a method for learning multiple tasks in parallel where at each round a sample is given per each task, but only a single task can have its sample annotated. The authors formulate their method using a trade-off between exploitation and exploration. Th1 provides an upper bound on the expected cumulative number of mistakes. The algorithm is compared to 2 different approaches for choosing the single sample/task to be annotated. It is well written and provides good theoretical as well as experimental results.