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

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

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.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper introduces a GP-Vol model to flexibly capture the time-dependent changes in variance, and develops a new online algorithm for fully Bayesian inference under the model. The paper is clearly written, the developed inference method seems technically sound, and the presented results look promising. My opinion on the model itself, using a non-parametric approach such as using the GP prior on the transition function (as in the paper), seems, though, a bit an obvious way of extending the prior work developed in the finance area. So, I wouldn't put too high grade on the paper in terms of its originality.

So, most of the learning happens under model misspeci-fication.

In this paper, we propose the first re-sampling based algorithm that is asymptotically optimal for several classes of possibly un-bounded parametric distributions.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. In this paper the authors propose a novel bi-clustering approach based on a message passing algorithm. The motivation is that most current bi-clustering techniques overcome the computational difficulty of the problem by performing greedy local optimisations. In this paper, the authors propose to overcome this by defining a global likelihood function (eq 1) and then maximising an approximation to this function via message passing. Quality: this is a high quality paper.