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

[ps.gz][pdf][bibtex]

Divergences, surrogate loss functions and experimental design

XuanLong Nguyen, Martin Wainwright, Michael Jordan [ps.gz][pdf][bibtex]

Generalization to Unseen Cases

Teemu Roos, Peter GrÃƒÂ¼nwald, Petri MyllymÃƒÂ¤ki, 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

[ps.gz][pdf][bibtex]

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]