Recent work in variational inference (VI) uses ideas from Monte Carlo estimation to tighten the lower bounds on the log-likelihood that are used as objectives.

I recommend moving the background section in a summarized form to the main paper while moving some of the proofs in the appendix. Unfortunately it was a delicate balancing act to fit both background material as well as our contribution within 8 pages. However, your point is well taken and we will try to fix this in the camera ready.

We present a technique for significantly speeding up Alternating Least Squares (ALS) and Gradient Descent (GD), two widely used algorithms for tensor factorization. By exploiting properties of the Khatri-Rao product, we show how to efficiently address a computationally challenging sub-step of both algorithms. Our algorithm, DFacTo, only requires two sparse matrix-vector products and is easy to parallelize. DFacTo is not only scalable but also on average 4 to 10 times faster than competing algorithms on a variety of datasets. For instance, DFacTo only takes 480 seconds on 4 machines to perform one iteration of the ALS algorithm and 1,143 seconds to perform one iteration of the GD algorithm on a 6.5 million 2.5 million 1.5 million dimensional tensor with 1.2 billion non-zero entries.

We study the problem of zero-order optimization of a strongly convex function.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper introduces max-margin Bayesian clustering (BMC) that extends Bayesian clustering techniques to include the max-margin criterion. This includes, for example, the Dirichlet process max-margin Gaussian mixture that relaxes the underlying Gaussian assumption of Dirichlet process Gaussian mixtures by incorporating max-margin posterior constraints, and is able to infer the number of clusters from data. The resulting techniques (DPMMGM and a further one classed MMCTM) are compared to a variety of other techniques in several numerical experiments. The paper combines two clustering approaches: Deterministic and Bayesian clustering.

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. The paper is concerned with Monte Carlo sampling based on the discretisation of SDEs. This is a particularly topical subject since there has been some interest lately in such techniques due to the fact that they allow for the use of stochastic gradients which are particularly appealing in some big data settings since they allow one to run algorithms with only partial evaluation of the likelihood/energy function. The paper is particularly well written and pedagogical. In additional it clarifies earlier contributions and provides a rigorous overview of the main results useful in this emerging area.

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