Call for Papers

The Seventeenth International Conference on Machine Learning

June 29-July 2, 2000, Stanford University


The Seventeenth International Conference on Machine Learning (ICML-2000) will be held at Stanford University from June 29 to July 2, 2000, in the heart of Silicon Valley. The conference will bring together researchers to exchange ideas and report recent progress in the computational study of learning.

Topics for Submission

ICML-2000 welcomes submissions on all facets of machine learning, but especially solicits papers on problem areas, research topics, learning paradigms, and approaches to evaluation that have been rare at recent conferences, including:

Naturally, we also welcome submissions on traditional topics, ranging from induction over supervised data to learning from delayed rewards, but we hope the conference will also attract contributions on the issues above.

Review Process

The ICML-2000 review process will be structured to encourage publications covering a broad range of research and to foster increased participation in the conference. To this end, we have instituted:

The overall goal is to make the review process more like that in journals, with time for the authors to incorporate feedback from reviewers. Each submitted paper will be reviewed by two members of the program committee, with the decision about its acceptance overseen by the responsible area chair and the program chair.

Paper Submission

Authors should submit papers using same format and length as the final proceedings version. The detailed instructions for authors at

http://www-csli.stanford.edu/icml2k/instructions.html

include pointers to templates for LaTeX and Word documents. These specify two-column style, Times Roman font with 10 point type, vertical spacing of 11 points, overall text width of 6.75 inches, length of 9.0 inches, 0.25 inches between the two columns, top margin of 1.0 inch, and left margin of 0.75 inch. (The right and bottom margins will depend on whether one uses US letter or A4 paper.) Papers must not exceed eight (8) pages including figures and references. We will return to the authors any papers that do not satisfy these requirements.

The deadline for submissions to ICML-2000 is Monday, January 31, 2000. Submission will be entirely electronic by transferring papers to the ICML-2000 ftp site, as explained in the detailed instructions for authors. Authors must submit papers in postscript format to ensure our ability to print them out for review.

Each submission must be accompanied by the paper's title, the authors' names and physical addresses, a 250-word abstract, the contact author's email address and phone number, and the author who would present the talk at the conference. Authors must enter this information into the submission form at the conference web site by Monday, January 24.

ICML-2000 allows simultaneous submission to other conferences, provided this fact is clearly indicated on the submission form. Accepted papers will appear in the conference proceedings only if they are withdrawn from other conferences. Simultaneous submissions that are not clearly specified as such will be rejected.

Other Conference Information

The Seventeenth International Conference on Machine Learning will be collocated with the Thirteenth Annual Conference on Computational Learning Theory (COLT-2000) and the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI-2000). Registrants to any of these meetings will be able to attend the technical sessions of the others at no additional cost.

ICML-2000 will also be preceded by tutorials on various facets of machine learning. For additional information, see the web site for the conference at

http://www-csli.stanford.edu/icml2k/
which will provide additional details as they become available. If you have questions about ICML-2000, please send electronic mail to icml2k@csli.stanford.edu.

The conference has received support from DaimlerChrysler Research and Technology, Stanford's Center for the Study of Language and Information (CSLI), and the Institute for the Study of Learning and Expertise (ISLE).