Yet more nips thoughts

I only managed to make it out to the NIPS workshops this year so
I’ll give my comments on what I saw there.

The Learing and Robotics workshops lives again. I hope it
continues and gets more high quality papers in the future. The
most interesting talk for me was Larry Jackel’s on the LAGR
program (see John’s previous post on said program). I got some
ideas as to what progress has been made. Larry really explained
the types of benchmarks and the tradeoffs that had to be made to
make the goals achievable but challenging.

Hal Daume gave a very interesting talk about structured
prediction using RL techniques, something near and dear to my own
heart. He achieved rather impressive results using only a very
greedy search.

The non-parametric Bayes workshop was great. I enjoyed the entire
morning session I spent there, and particularly (the usually
desultory) discussion periods. One interesting topic was the
Gibbs/Variational inference divide. I won’t try to summarize
especially as no conclusion was reached. It was interesting to
note that samplers are competitive with the variational
approaches for many Dirichlet process problems. One open question
I left with was whether the fast variants of Gibbs sampling could
be made multi-processor as the naive variants can.

I also have a reading list of sorts from the main
conference. Most of the papers mentioned in previous posts on
NIPS are on that list as well as these: (in no particular order)

The Information-Form Data Association Filter
Sebastian Thrun, Brad Schumitsch, Gary Bradski, Kunle Olukotun

Divergences, surrogate loss functions and experimental design
XuanLong Nguyen, Martin Wainwright, Michael Jordan [ps.gz][pdf][bibtex]

Generalization to Unseen Cases
Teemu Roos, Peter Grünwald, Petri Myllymäki, Henry Tirri [ps.gz][pdf][bibtex]

Gaussian Process Dynamical Models
David Fleet, Jack Wang, Aaron Hertzmann [ps.gz][pdf][bibtex]

Convex Neural Networks
Yoshua Bengio, Nicolas Le Roux, Pascal Vincent, Olivier Delalleau,
Patrice Marcotte [ps.gz][pdf][bibtex]

Describing Visual Scenes using Transformed Dirichlet Processes
Erik Sudderth, Antonio Torralba, William Freeman, Alan Willsky

Learning vehicular dynamics, with application to modeling helicopters
Pieter Abbeel, Varun Ganapathi, Andrew Ng [ps.gz][pdf][bibtex]

Tensor Subspace Analysis
Xiaofei He, Deng Cai, Partha Niyogi [ps.gz][pdf][bibtex]