First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The authors combine two recent advances in Gaussian processes, spectral mixture kernels [5], and scalable Gaussian processes for data in grids [14, 22 see below], in order to tackle applications with high amount of data points, like texture extrapolation, inpainting, and video extrapolation. The paper includes a thorough evaluation of the framework proposed, and comparisons against sparse GP methods, with general purpose covariance functions, and spectral mixture kernels. Quality The paper is technically sound. The framework proposed by the authors achieves outstanding results in the different applications studied in the paper.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Paper Summary: This paper treats a general multi-armed bandit problem in which the mean reward of each arm depends on a common unknown parameter. The authors consider a simple modification of the UCB1 algorithm. They show, unsurprisingly, that the algorithm satisfies a regret bound like that of UCB1. The main improvement of this paper is to show when the optimal arm can be identified perfectly by samples of the optimal arm, algorithm's regret is bounded by a constant independent of the time horizon.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Line 33: I don't think it is accurate to attribute the recent success of supervised neural nets on various applications to BP and dropout. Firstly, learning nets with gradient descent has been around a long time, and the key to its recent success has mostly been fast computers/GPUs, a wealth of labelled data, advances in understanding of how to make SGD work well (e.g. Techniques like dropout have also been useful in reducing overfitting, but are hardly the key missing ingredient to make these systems work well. Line 37: The claim that the lacklustre the results associated with unsupervised generative approaches is owed purely to their intractability issues is a strong and problematic one.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The authors present a variational approach L-FIELD to general log-submodular and supermodular distributions. Theoretical contributions include deriving upper and lower bounds on the log-partition function and fully factorized approximate posteriors. The quality of the approximation is tested with respect to the curvature of the function. Empirical results are presented on GMM cuts and MRFs, decomposable functions and facility location modeling.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper considers the setting of a sensor network (or agents) in a noisy environment that are able to communicate locally. The authors prove that theoretical bounds on the number of active queries can be achieved through simple best response dynamics. The paper is very well-written, technically correct, and the synthetic experiment makes the results clear. The theoretical results are novel and I think that the paper deserves to be published to be published at NIPS.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper reduces a broad class of machine learning problems involving latent variables to the problem of finding anchors defining the conical hull of the data (via the method of moments). In addition, it proposes a new divide-and-conquer algorithm based on random projections to speed up the search for the anchors. Overall, I found this an interesting paper presenting significant contributions. However the presentation could be greatly improved as it lacks clarity here and there. It looks like this paper was squeezed in a hurry to fit the 8-page limit.

The authors discuss how the problems can be formulated as optimization of objective functions defined on the subgraphs. A straightforward search over the subgraphs is computationally infeasible, so the authors present a highly novel approach that leads to computationally efficient tests. The paper includes proofs that the tests are nearly minimax optimal for the exponential family of distributions and graphs satisfying the polynomial growth property. The paper concludes with an analysis of synthetic and real datasets. Strengths: (1) The paper addresses a problem of growing importance and presents novel approaches for statistical tests.

"NIPS Neural Information Processing Systems 8-11th December 2014, Montreal, Canada",,, "Paper ID:","1612" "Title:","Improved Distributed Principal Component Analysis" Current Reviews First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper considers the problem of trading off the communication and computation cost of distributed computation and proposes a new distributed k L-2 error fitting algorithm. The proposed algorithm can be seen as a combination of many previous speed up techniques for distributed PCA and clustering methods. However, the authors also contribute optimizations over the base methods and further improves the communication and computation efficiency. The theoretical guarantee is sound and experiments are convincing.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper studies the estimation of the k-dimensional principal subspace of a population matrix based on sample covariance matrix. Two estimators based on convex and non-convex optimizations are developed for projection matrix with large or small magnitude entries, respectively. Both these two estimators are shown to enjoy satisfactory theoretical properties and experimental results compared with state-of-the-art estimators. It would be better to clearly explain what the oracle knowledge used in the proposed algorithm is, and how to set up the oracle estimator comparison experiments.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper introduces a novel convex region-specific linear models called partition-wise linear model. It assigns linear models to partitions of the input space and linear combination of these partition-specific models define the region-specific linear models. This allows them to construct convex objective functions. They optimize both the regions and predictors by using sparsity inducing structured penalties.