Welcome! Please contribute your ideas for what challenges we might aspire to solve, changes in our community that can improve machine learning impact, and examples of machine learning projects that have had tangible impact.
Lacking context for this site? Read the original paper: Machine Learning that Matters (ICML 2012). You can also review the slides.
Past efforts
  • Some of us are involved in applications and over the years have tried to get ICML to be more involved in applications.  Back in 1995 several of us wrote an open letter to the ML commununity on why the community should be interested in applications papers (see http://home.comcast.net/~tom.fawcett/public_html/papers/essay.html).  Though our orientation there was slightly different, you'll notice some of the same points that Kiri makes.

    Some of us have tried to change ICML over the years.  Pat Langley was Program Chair in 2000 and I was in 2003; we each tried to make the conference more accommodating to applications papers.  Also, various of us have been area chairs for applications.  A lot of things have been tried in the past 15 years.  It's probably worth digging up some of this history.
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  • I agree, Tom.  And ICML has experimented with different ways to try to accomplish this, with varying success.  I think a retrospective analysis of what has worked and what hasn't would be excellent context for ideas going forward.  And at the same time, we might think about why this has been such a struggle.  Are those of us interested in applications in the minority?  Are applications papers simply harder to do well, so they don't get accepted?
  • I can only speak for myself but:  after receiving so many negative comments for being applications-oriented, I simply stopped caring about ICML.  I won't attend again unless things change.  It isn't worth it for me.  I also stopped reviewing for them after I co-reviewed with someone who argued very firmly that an applications paper for ICML had to be rejected because it hadn't changed the world so it wasn't good enough.  

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