Leading ML researchers issue statement of support for JMLR

From: Michael Jordan [mailto:jordan@CS.Berkeley.EDU]
Sent: Monday, October 08, 2001 5:33 PM
Subject: letter of resignation from Machine Learning journal

Dear colleagues in machine learning,

The forty people whose names appear below have resigned from the
Editorial Board of the Machine Learning Journal (MLJ).  We would
like to make our resignations public, to explain the rationale for
our action, and to indicate some of the implications that we see for
members of the machine learning community worldwide.

The machine learning community has come of age during a period
of enormous change in the way that research publications are
circulated.  Fifteen years ago research papers did not circulate
easily, and as with other research communities we were fortunate
that a viable commercial publishing model was in place so that
the fledgling MLJ could begin to circulate.  The needs of the
community, principally those of seeing our published papers circulate
as widely and rapidly as possible, and the business model of
commercial publishers were in harmony.

Times have changed.  Articles now circulate easily via the Internet,
but unfortunately MLJ publications are under restricted access.
Universities and research centers can pay a yearly fee of $1050 US to
obtain unrestricted access to MLJ articles (and individuals can pay
$120 US).  While these fees provide access for institutions and
individuals who can afford them, we feel that they also have the
effect of limiting contact between the current machine learning
community and the potentially much larger community of researchers
worldwide whose participation in our field should be the fruit of
the modern Internet.

None of the revenue stream from the journal makes its way back to
authors, and in this context authors should expect a particularly
favorable return on their intellectual contribution---they should
expect a service that maximizes the distribution of their work.
We see little benefit accruing to our community from a mechanism
that ensures revenue for a third party by restricting the communication
channel between authors and readers.

In the spring of 2000, a new journal, the Journal of Machine Learning
Research (JMLR), was created, based on a new vision of the journal
publication process in which the editorial board and authors retain
significant control over the journal's content and distribution.
Articles published in JMLR are available freely, without limits and
without conditions, at the journal's website, http://www.jmlr.org.
The content and format of the website are entirely controlled by the
editorial board, which also serves its traditional function of
ensuring rigorous peer review of journal articles.  Finally, the
journal is also published in a hardcopy version by MIT Press.

Authors retain the copyright for the articles that they publish in
JMLR.  The following paragraph is taken from the agreement that every
author signs with JMLR (see www.jmlr.org/forms/agreement.pdf):

  You [the author] retain copyright to your article, subject only
  to the specific rights given to MIT Press and to the Sponsor [the
  editorial board] in the following paragraphs.  By retaining your
  copyright, you are reserving for yourself among other things unlimited
  rights of electronic distribution, and the right to license your work
  to other publishers, once the article has been published in JMLR
  by MIT Press and the Sponsor [the editorial board].  After first
  publication, your only obligation is to ensure that appropriate
  first publication credit is given to JMLR and MIT Press.

We think that many will agree that this is an agreement that is
reflective of the modern Internet, and is appealing in its recognition
of the rights of authors to distribute their work as widely as possible.
In particular, authors can leave copies of their JMLR articles on their
own homepage.

Over the years the editorial board of MLJ has expanded to encompass
all of the various perspectives on the machine learning field, and
the editorial board's efforts in this regard have contributed greatly
to the sense of intellectual unity and community that many of us feel.
We believe, however, that there is much more to achieve, and that
our further growth and further impact will be enormously enhanced
if via our flagship journal we are able to communicate more freely,
easily, and universally.

Our action is not unprecedented.  As documented at the Scholarly Publishing
and Academic Resources Coalition (SPARC) website, http://www.arl.org/sparc,
there are many areas in science where researchers are moving to low-cost
publication alternatives.  One salient example is the case of the
journal "Logic Programming".  In 1999, the editors and editorial
advisors of this journal resigned to join "Theory and Practice of
Logic Programming", a Cambridge University Press journal that encourages
electronic dissemination of papers.

In summary, our resignation from the editorial board of MLJ reflects
our belief that journals should principally serve the needs of the
intellectual community, in particular by providing the immediate and
universal access to journal articles that modern technology supports,
and doing so at a cost that excludes no one.  We are excited about JMLR,
which provides this access and does so unconditionally.  We feel that
JMLR provides an ideal vehicle to support the near-term and long-term
evolution of the field of machine learning and to serve as the flagship
journal for the field.  We invite all of the members of the community
to submit their articles to the journal and to contribute actively to
its growth.

Sincerely yours,

  Chris Atkeson
  Peter Bartlett
  Andrew Barto
  Jonathan Baxter
  Yoshua Bengio
  Kristin Bennett
  Chris Bishop
  Justin Boyan
  Carla Brodley
  Claire Cardie
  William Cohen
  Peter Dayan
  Tom Dietterich
  Jerome Friedman
  Nir Friedman
  Zoubin Ghahramani
  David Heckerman
  Geoffrey Hinton
  Haym Hirsh
  Tommi Jaakkola
  Michael Jordan
  Leslie Kaelbling
  Daphne Koller
  John Lafferty
  Sridhar Mahadevan
  Marina Meila
  Andrew McCallum
  Tom Mitchell
  Stuart Russell
  Lawrence Saul
  Bernhard Schoelkopf
  John Shawe-Taylor
  Yoram Singer
  Satinder Singh
  Padhraic Smyth
  Richard Sutton
  Sebastian Thrun
  Manfred Warmuth
  Chris Williams
  Robert Williamson