I thought this was a very good NIPS with many excellent papers. The following are a few NIPS papers which I liked and I hope to study more carefully when I get the chance. The list is not exhaustive and in no particular order…

- Preconditioner Approximations for Probabilistic Graphical Models.

Pradeeep Ravikumar and John Lafferty.

I thought the use of preconditioner methods from solving linear systems in the context of approximate inference was novel and interesting. The results look good and I’d like to understand the limitations. - Rodeo: Sparse nonparametric regression in high dimensions.

John Lafferty and Larry Wasserman.

A very interesting approach to feature selection in nonparametric regression from a frequentist framework. The use of lengthscale variables in each dimension reminds me a lot of ‘Automatic Relevance Determination’ in Gaussian process regression — it would be interesting to compare Rodeo to ARD in GPs. - Interpolating between types and tokens by estimating power law generators.

Goldwater, S., Griffiths, T. L., & Johnson, M.

I had wondered how Chinese restaurant processes and Pitman-Yor processes related to Zipf’s plots and power laws for word frequencies. This paper seems to have the answers. - A Bayesian spatial scan statistic.

Daniel B. Neill, Andrew W. Moore, and Gregory F. Cooper.

When I first learned about spatial scan statistics I wondered what a Bayesian counterpart would be. I liked the fact they their method was simple, more accurate, and much*faster*than the usual frequentist method. - Q-Clustering.

M. Narasimhan, N. Jojic and J. Bilmes.

A very interesting application of sub-modular function optimization to clustering. This feels like a hot area. - Worst-Case Bounds for Gaussian Process Models.

Sham M. Kakade, Matthias W. Seeger, & Dean P. Foster.

It’s useful for Gaussian process practitioners to know that their approaches don’t do silly things when viewed from a worst-case frequentist setting. This paper provides some relevant theoretical results.