Connectionists: [Reminder: Abstracts due 10/31]: Call for Abstracts: NIPS 2007 Workshop on Statistical Learning Techniques for Solving Systems Problems

[MLSys'07 Workshop Organizers]

[Reminder] Call for Abstracts:  NIPS 2007 Workshop on Statistical
Learning Techniques for Solving Systems Problems (1-2 page abstracts due
10/31)

In the last few years, there has been a budding interaction between
machine learning and computer systems researchers. In particular,
statistical machine learning techniques have found a wide range of
successful applications in many core systems areas, from designing
computer microarchitectures and analyzing network traffic patterns to
managing power consumption in data centers and beyond. However,
connecting these two areas has its challenges: while systems problems
are replete with mountains of data and hidden variables, complex sets of
interacting systems, and other exciting properties, labels can be hard
to come by, and the measure of success can be hard to define.
Furthermore, systems problems often require much more than high
classification accuracy - the answers from the algorithms need to be
both justifiable and actionable. Dedicated workshops in systems
conferences have emerged (for example, SysML 2006 and SysML 2007) to
address this area, though they have had little visibility to the machine
learning community. A primary goal of this workshop is thus to expose
these new research opportunities in systems areas to machine learning
researchers, in the hopes of encouraging deeper and broader synergy
between the two communities. During the workshop, through various
planned overviews, invited talks, poster sessions, group discussions,
and panels, we would like to achieve three objectives. First, we wish to
discuss the unique opportunities and challenges that are inherent to
this area. Second, we want to discuss and identify "low-hanging fruit"
that are be more easily tackled using existing learning techniques.
Finally, we will cover how researchers in both areas can make rapid
progress on these problems using existing toolboxes for both machine
learning and systems. We hope that this workshop will present an
opportunity for intensive discussion of existing work in machine
learning and systems, as well as inspire a new generation of researchers
to become involved in this exciting domain.

Call for Abstracts:  We are seeking 2-page abstracts about recent work
at the intersection of machine learning and systems.  We welcome
preliminary work and work you may plan to publish at a later conference:
we are intentionally not creating proceedings for this workshop so that
authors are free to submit work to later venues.  However, if there is
sufficient interest we will explore the possibility of a special issue
of a journal or a book seeded with selected papers from the workshop.

Please email your abstracts in PDF format to:  mlsys07 at yahoogroups.com
by  October 31, 2007.  We will select abstracts for presentation at the
workshop by November 7, 2007.    Please send any questions you may have
about the workshop to this address as well.  We look forward to hearing
from you!

Sincerely, 

  Archana Ganapathi <archanag at eecs.berkeley.edu>
  Sumit Basu <sumitb at microsoft.com>
  Emre Kiciman <emrek at microsoft.com>
  Fei Sha <feisha at yahoo-inc.com>

More information is available at http://radlab.cs.berkeley.edu/MLSys