Somehow I totally missed NIPS workshops!

I don't know how it happened or when it happened, but at some point NIPS workshops were posted and papers are due about a week from now and I completely missed it!  The list of workshops is here:

    http://nips.cc/Conferences/2012/Program/schedule.php?Session=Workshops

Since my job as a blogger is to express my opinion about things you don't want to hear my opinion about, I wish they'd select fewer workshops.  I've always felt that NIPS workshops are significantly better than *ACL workshops because they tend to be workshops and not mini-conferences (where "mini" is a euphemism for non-selective :P).  At NIPS workshops people go, really talk about problems and it's really the best people and the best work in the area.  And while, yes, it's nice to be supportive of lots of areas, but what ends up happening is that people jump between workshops because there are too many that interest them, and then you lose this community feeling.  This is especially troubling when workshops are already competing with skiing :).

Anyway, with that behind me, there are a number that NLP folks might find interesting:

• Algorithmic and Statistical Approaches for Large Social Networks: Social networks aren't that big of a topic in NLP, but I think it's potentially a space we'll move in to and overlap with, especially as more and more text becomes available on such networks.
  
• Discrete Optimization in Machine Learning: Structure and Scalability: One of the things that's perhaps relevant to our field but approached from a different perspective.
  
• Social network and social media analysis: Methods, models and applications: Another social network thing, perhaps focused more on media than the networks
  
• Spectral Learning Workshop: Spectral methods have made a bit of a splash in ACL land and there's definitely lots of interesting work to do here.
  
• Cross-Lingual Technologies: This is probably the closest to our hearts. Anything multilingual!!!
  
• Big Learning: New Perspectives, Implmentations and Challenges: Who doesn't like big learning. I'm still not convinced that the ML notion of "big" is as big as our notion of "big" but it's definitely getting there!
  
• Personalizing Education With Machine Learning: I don't think there's much in the way of NLP in personalizing education, but I see this as a potential big avenue for large breakthroughs in NLP in the coming 5 years, especially as we start talking about automated grading in MOOCs and whatnot. ETS folks, I hear you!

With the deadlines so close I don't imagine anyone's going to be submitting stuff that they just started, but if you have things that already exist, NIPS is fun and it would be fun to see more NLPers there!