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September 25, 2008

Workshop: Analyzing Graphs, Theory and Applications

The only time I've ever been to NIPS [1] was to present the results of my first research project in grad school [2]. It was a fun trip, especially because the NIPS workshops are held at the Whistler ski resort [3]. The NIPS conference is now home to a lot of machine learning research, and this year I'm helping out with a workshop on the methodological side of network analysis. Although I won't be able to actually attend the workshop, I have high hopes for it [4], as methodological questions are pretty fundamental to our ability to say both interesting and useful things about networks, and their relevance to the many branches of science that now use them. So, get those TeX compilers humming!

NIPS 2008 Workshop on Analyzing Graphs: Theory and Applications

December 12, 2008 at NIPS at Whistler Canada

Organizers: Edo Airoldi (Princeton), David Blei (Princeton), Jake Hofman (Yahoo! Research), Tony Jebara (Columbia U.), and Eric Xing (CMU).

Submission Deadline: Friday, October 31, 2008

Description: Recent research in machine learning and statistics has seen the proliferation of computational methods for analyzing graphs and networks. These methods support progress in many application areas, including the social sciences, biology, medicine, neuroscience, physics, finance, and economics.

This workshop will address statistical, methodological and computational issues that arise when modeling and analyzing graphs. The workshop aims to bring together researchers from applied disciplines such as sociology, economics, medicine and biology with researchers from mathematics, physics, statistics and computer science. Different communities use diverse ideas and mathematical tools; our goal is to foster cross-disciplinary collaborations and intellectual exchange.

We welcome the following types of papers:
- Research papers that introduce new models or apply established models to novel domains,
- Research papers that explore theoretical and computational issues, or
- Position papers that discuss shortcomings and desiderata of current approaches, or propose new directions for future research.

All submissions will be peer-reviewed; exceptional work will be considered for oral presentation. We encourage authors to emphasize the role of learning and its relevance to the application domains at hand. In addition, we hope to identify current successes in the area, and will therefore consider papers that apply previously proposed models to novel domains and data sets.


[1] NIPS stands for Neural Information Processing Systems, but has become one of the main machine learning conferences.

[2] Which is still unpublished, not because it's wrong or bad, but because I'm lazy. I like to think that I'm saving it for a "rainy day", but who am I really kidding?

[3] Of course, on that trip, like a fool, I didn't ski at all. Just presented my results, tromped around in the snow, ate some good food, and did almost all the work for a new paper on the bus and plane back to New Mexico.

[4] This is partly because I'm friends with many of the organizers, who care about many of the same things I do in terms of methodological accuracy.

posted September 25, 2008 04:41 PM in Conferences and Workshops | permalink