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ICML 2009 Workshops (and Tutorials)

I’m the workshops chair for ICML this year. As such, I would like to personally encourage people to consider running a workshop.

My general view of workshops is that they are excellent as opportunities to discuss and develop research directions—some of my best work has come from collaborations at workshops and several workshops have substantially altered my thinking about various problems. My experience running workshops is that setting them up and making them fly often appears much harder than it actually is, and the workshops often come off much better than expected in the end. Submissions are due January 18, two weeks before papers.

Similarly, Ben Taskar is looking for good tutorials, which is complementary. Workshops are about exploring a subject, while a tutorial is about distilling it down into an easily taught essence, a vital part of the research process. Tutorials are due February 13, two weeks after papers.

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NYAS ML Symposium this year.

The NYAS ML symposium grew again this year to 170 participants, despite the need to outsmart or otherwise tunnel through a crowd.

Perhaps the most distinct talk was by Bob Bell on various aspects of the Netflix prize competition. I also enjoyed several student posters including Matt Hoffman‘s cool examples of blind source separation for music.

I’m somewhat surprised how much the workshop has grown, as it is now comparable in size to a small conference, although in style more similar to a workshop. At some point as an event grows, it becomes owned by the community rather than the organizers, so if anyone has suggestions on improving it, speak up and be heard.

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NIPS workshops

Many of the NIPS workshops have a deadline about now, and the NIPS early registration deadline is Nov. 6. Several interest me:

  1. Adaptive Sensing, Active Learning, and Experimental Design due 10/27.
  2. Discrete Optimization in Machine Learning: Submodularity, Sparsity & Polyhedra, due Nov. 6.
  3. Large-Scale Machine Learning: Parallelism and Massive Datasets, due 10/23 (i.e. past)
  4. Analysis and Design of Algorithms for Interactive Machine Learning, due 10/30.

And I’m sure many of the others interest others. Workshops are great as a mechanism for research, so take a look if there is any chance you might be interested.

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Many ways to Learn this summer

There are at least 3 summer schools related to machine learning this summer.

  1. The first is at University of Chicago June 1-11 organized by Misha Belkin, Partha Niyogi, and Steve Smale. Registration is closed for this one, meaning they met their capacity limit. The format is essentially an extended Tutorial/Workshop. I was particularly interested to see Valiant amongst the speakers. I’m also presenting Saturday June 6, on logarithmic time prediction.
  2. Praveen Srinivasan points out the second at Peking University in Beijing, China, July 20-27. This one differs substantially, as it is about vision, machine learning, and their intersection. The deadline for applications is June 10 or 15. This is also another example of the growth of research in China, with active support from NSF.
  3. The third one is at Cambridge, England, August 29-September 10. It’s in the MLSS series. Compared to the Chicago one, this one is more about the Bayesian side of ML, although effort has been made to create a good cross section of topics. It’s also more focused on tutorials over workshop-style talks.
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Interesting Presentations at Snowbird

Here are a few of presentations interesting me at the snowbird learning workshop (which, amusingly, was in Florida with AIStat).

  1. Thomas Breuel described machine learning problems within OCR and an open source OCR software/research platform with modular learning components as well has a 60Million size dataset derived from Google‘s scanned books.
  2. Kristen Grauman and Fei-Fei Li discussed using active learning with different cost labels and large datasets for image ontology. Both of them used Mechanical Turk as a labeling system, which looks to become routine, at least for vision problems.
  3. Russ Tedrake discussed using machine learning for control, with a basic claim that it was the way to go for problems involving a medium Reynold’s number such as in bird flight, where simulation is extremely intense.
  4. Yann LeCun presented a poster on an FPGA for convolutional neural networks yielding a factor of 100 speedup in processing. In addition to the graphics processor approach Rajat has worked on, this seems like an effective approach to deal with the need to compute many dot products.