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More generally, this is a good time to ask for suggestions. What would make this blog more useful?

## John Langford –> Yahoo Research, NY

I will join Yahoo Research (in New York) after my contract ends at TTI-Chicago.

The deciding reasons are:

- Yahoo is running into many hard learning problems. This is precisely the situation where basic research might hope to have the greatest impact.
- Yahoo Research understands research including publishing, conferences, etc…
- Yahoo Research is growing, so there is a chance I can help it grow well.
- Yahoo understands the internet, including (but not at all limited to) experimenting with research blogs.

In the end, Yahoo Research seems like the place where I might have a chance to make the greatest difference.

Yahoo (as a company) has made a strong bet on Yahoo Research. We-the-researchers all hope that bet will pay off, and this seems plausible. I’ll certainly have fun trying.

## 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

[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]

## Yes , I am applying

Every year about now hundreds of applicants apply for a research/teaching job with the timing governed by the university recruitment schedule. This time, it’s my turn—the hat’s in the ring, I am a contender, etc… What I have heard is that this year is good in both directions—both an increased supply and an increased demand for machine learning expertise.

I consider this post a bit of an abuse as it is neither about general research nor machine learning. Please forgive me this once.

My hope is that I will learn about new places interested in funding basic research—it’s easy to imagine that I have overlooked possibilities.

I am not dogmatic about where I end up in any particular way. Several earlier posts detail what I think of as a good research environment, so I will avoid a repeat. A few more details seem important:

- Application. There is often a tension between basic research and immediate application. This tension is not as strong as might be expected in my case. As evidence, many of my coauthors of the last few years are trying to solve particular learning problems and I strongly care about whether and where a learning theory is useful in practice.
- Duration. I would like my next move to be of indefinite duration.

Feel free to email me (jl@hunch.net) if there is a possibility you think I should consider.

## Machine Learning Thoughts

I added a link to Olivier Bousquet’s machine learning thoughts blog. Several of the posts may be of interest.