First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper presents a Bayesian approach to state and parameter estimation in nonlinear state-space models, while also learning the transition dynamics through the use of a Gaussian process (GP) prior. The inference mechanism is based on particle Markov chain Monte Carlo (PMCMC) with the recently-introduced idea of ancestor sampling. The paper also discusses computational efficiencies to be had with respect to sparsity and low-rank Cholesky updates. This is a technically sound and strong paper with clear and accessible presentation.

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

Jaccard similarity to be practical in large-scale settings.

The fast convergence holds in layer-wise approximations; for instance, in block diagonal approximation where each block corresponds to a layer as well as in block tri-diagonal and K-FAC approximations.

Interfaces based on rankings are ubiquitous in today's multi-sided online economies (e.g., online marketplaces, job search, property renting, media streaming).

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper describes a method for local bandwidth selection in kernel regression models, which ensures adaptivity to local smoothness and dimension. Quality: The paper presents a useful result for adaptivity in kernel regression. The work is set out well. I think that it would be useful to have some more discussion of the bandwidth selection procedure.