Call for Papers
The 29th International Conference on Machine Learning (ICML 2012) will be held at the University of Edinburgh, Scotland, from June 26 to July 1 2012.
ICML 2012 invites the submission of engaging papers on substantial, original, and previously unpublished research in all aspects of machine learning. We welcome submissions of innovative work on systems that are self adaptive, systems that improve their own performance, or systems that apply logical, statistical, probabilistic or other formalisms to the analysis of data, to the learning of predictive models, to cognition, or to interaction with the environment. We welcome innovative applications, theoretical contributions, carefully evaluated empirical studies, and we particularly welcome work that combines all of these elements. We also encourage submissions that bridge the gap between machine learning and other fields of research.
- Workshop and tutorial proposals due February 10, 2012
- Paper submissions due February 24, 2012
- Author response period April 9–12, 2012
- Author notification April 30, 2012
- Workshop submissions due May 7, 2012
- Workshop author notification May 21, 2012
- Tutorials June 26, 2012
- Main conference June 27–29, 2012
- Workshops June 30–July 1, 2012
The conference will include three days of technical presentations, one day of tutorials, and two days of workshops. In addition, this year the conference will co-located with the 25th Annual Conference on Learning Theory (COLT 2012).
Accepted papers will each have an oral presentation as well as a poster in an evening poster session. Awards will be given for papers of outstanding quality. There will also be talks by several invited speakers, and a banquet.
The submission of papers and the management of the paper reviewing process for the main conference will be entirely electronic. Submissions will be accepted until Friday February 24, 23:59 Universal Time (3:59pm Pacific Daylight Time). Detailed formatting and submission instructions for authors are available on the conference web site.
ICML 2012 will not accept any paper for publication that is substantially similar to another paper that is currently under review or has already been accepted for publication in a journal or another conference. Note that we do have a 'not for publication' submission option which covers some of these cases—see the double submission guide for details.
Please clearly indicate in the submission which contributions are novel and which are previous work, either by the authors or others. If a paper submitted to ICML 2012 and another already published or already submitted paper contain substantial overlap in content and this overlap is not clearly indicated (anonymously) as being previous work, then the ICML submission may be rejected on the grounds of being a dual submission. Similarly, authors must withdraw their papers if they submit an overlapping paper elsewhere during the ICML review period. For papers published in substantially disjoint communities (for example, area conferences where ML is often a core technology), the amount of novel content a paper needs to contain may be less, as long as the submitted papers are themselves clearly targeted to a machine-learning audience.
For more details, see the ICML double submission guide.
The review process this year incorporates several improvements. Authors, reviewers, and area chairs indicate subject areas. With the help of these subject areas, area chairs and reviewers bid for papers. A first reviewer for each paper will be selected from those bids. New this year, two area chairs will also be assigned to oversee reviewing of each paper. Each area chair will manually appoint an additional reviewer for each paper using input from the bids. Authors will have the opportunity to see and respond to the reviews (and optionally revise their paper) before a final decision is made. Final decisions will be made using the input from all reviewers, the author feedback, the area chairs, and the program chairs. Reviewing for ICML 2012 is double blind between authors and reviewers.