SRL2004: Statistical Relational Learning and
its Connections to Other Fields

ICML 2004 Workshop

July 8, 2004


Important Dates

Apr 2 Submissions Due
Apr 19 Author Notification
May 7 Final Papers Due
July 8 (ICML/UAI overlap day) Workshop


Contact Information
Mailing List

Statistical machine learning is in the midst of a "relational revolution". After many decades of focusing on independent and identically-distributed (iid) examples, many researchers are now studying problems in which the examples are linked together into complex networks. These networks can be a simple as sequences and 2-D meshes (such as those arising in part-of-speech tagging and remote sensing) or as complex as citation graphs, the world wide web, and relational data bases.

There have been several workshops on relational learning in recent years. The goal of this workshop is to reach out to related fields that have not participated in previous workshops. Specifically, we seek to invite researchers in computer vision, spatial statistics, social network analysis, language modeling and probabilistic inference to attend the workshop and give tutorials on the relational learning problems and techniques developed in their fields.


Because our goal is to build links with other fields, a significant amount of time in the workshop will be devoted to invited tutorials and discussion. Tutorials will include (a) overview of relational learning, (b) relational learning in spatial statistics, (c) relational learning in social network analysis, (d) relational learning in computer vision, and (e) approximate probabilistic inference for large networks. Focus topics for discussion will include (a) methodology (e.g., how to evaluate machine learning research on linked data by using connections between the test set and training set in a principled manner). (b) barriers to progress (the cost of inference, the need for benchmark data sets), and (c) new application directions (short talks describing interesting new applications). To give participants an opportunity to share their research with others, we plan to have at least one poster session.

Participation is open. The registration code is srl2004
Please contact the organizers at if you sign up, to help us keep track of resigistration for the workshop.

Participants are encouraged to submit papers (maximum 6 pages). Please send your submission (PDF preferred) to Accepted papers may be presented orally or as posters.

Invited Speakers
Michael Collins MIT Abstract
Mark Handcock University of Washington Abstract
David Heckerman Microsoft Research Abstract
Daniel Huttenlocher Cornell University Abstract
David Poole University of British Columbia Abstract
Workshop Co-chairs
Tom Dietterich Oregon State University
Lise Getoor University of Maryland, College Park
Kevin Murphy MIT AI Lab
Program Committee
James Cussens University of York, UK
Luc De Raedt Albert-Ludwigs-University, Germany
Pedro Domingos University of Washington, USA
David Heckerman Microsoft, USA
David Jensen University of Massachusetts, Amherst, USA
Michael Jordan University of California, Berkeley, USA
Kristian Kersting Albert-Ludwigs-University, Germany
Daphne Koller Stanford University, USA
Andrew McCallum University of Massachusetts, Amherst, USA
Foster Provost NYU, USA
Dan Roth University of Illinois, Urbana-Champaign, USA
Stuart Russell University of California, Berkeley, USA
Taisuke Sato Tokyo Institute of Technology, Japan
Jeff Schneider Carnegie Mellon University, USA
Padhraic Smyth University of California, Irvine, USA
Ben Taskar Stanford University, USA
Lyle Ungar University of Pennsylvania, USA