Originality A major contribution of the paper is posing image set summarization as a submodular optimization problem; to the knowledge of this reviewer, this is a novel view of the problem. Together with a new dataset and the adaptation of ROUGE to a significantly different application domain, this paper has several novel contributions to the state of the art. Significance According to this reviewer, this work fits well in the topics of interest for NIPS, to which it makes a significant contribution. Q2: Please summarize your review in 1-2 sentences The paper presents an elegant formulation of the problem of image collection summarization along with a new dataset and an evaluation metric.

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

We sampled 100 GP prior functions from zero mean and unit variance. After realizing them, the prior functions are used to optimize via Bayesian optimization. All the experiments are implemented with [8]. Same as Appendix B.1, except that all the models were trained for 200 The other details are the same as in Appendix B.1. Seen classes (0-9) Unseen classes (10-46) t -noise CE sharpness CE Sharpness CE Sharpness CNP 0.448 We also measure the sharpness [10] which essentially is a average prediction variance.

Stochastic classifiers arise in a number of machine learning problems, and have become especially prominent of late, as they often result from constrained optimization problems, e.g. for fairness, churn, or custom losses.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Summary: This paper presents a provable guarantee under what conditions the convex optimization procedure (COP) can successfully recover the correct clustering solutions. The main result is: if the samples are drawn from two cubes, each being a cluster, then COP can obtain the correct clustering solution provided the distance between two cubes is larger than a threshold value that linearly depends on the cube size and the ratio of numbers of samples in each cluster. The proof is based on the idea of lifting, which projects the problem into a higher dimensional space that transforms the original formulation into a separable form (separating the regularization term into the sum of l_2 norm of each row). After constructing the optimal dual solution through some algebraic operations, the primal optimal solution can be obtained.

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 derives policy gradient algorithms for risk-sensitive MDPs for the particular criterion CVaR - a recent and popular criterion. First, the author derive gradients for the objective based on a Lagrangian relaxation of the constrained optimization. This naturally turns into a policy gradient algorithm where the expected return that appears in the gradient is estimated from full trajectories (reinforce-like). They then propose a scheme to obtain incremental actor-critic versions, where the critic computes the value (and other quantities) of an augmented MDP convenient for gradient estimation.