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ICML 2010 - Awards

Every year, ICML honors its best contributions with best paper awards. In 2010, the selection committee chose the contributions listed below. All best paper award winners received a certificate and a check for $1000, and all runner-ups received a check for $500. We are grateful to Springer, the publishers of Machine Learning, for helping to fund these awards.

Best Paper Award

For the Best Paper Award, we prepared a list of 19 papers that received excellent reviews from the reviewers and the area chair. In a two-step selection process, the following paper emerged as the clear winner of the ICML 2010 Best Paper Award:

The runner-up in this category was the following paper:

Best Student Paper Award

The best student papers were selected from the same 19 papers as the best paper awards, again in a two-step selection process. Only those papers that had a student as the primary author were eligible. The following paper won the best student paper award:

The close runner-up was:

Best Application Paper Award

For the best application, we formed a short list of six candidates with a strong focus on applications. We were excited to give the award to a paper that brings together basic research in transfer learning and cognitive science:

The runner-up in this category was:

Best 10-Year Paper

Last but not least, we had the privilege of honoring the most influential paper of ICML 2000 (Stanford). In a first phase, we selected a short list of six papers based on suggestions from the best paper committee and several senior members of the community. We then invited all area chairs of ICML 2010 and all members of the IMLS board to name and rank their three favorite papers among these six. From this vote, the following paper emerged as a clear winner:

  • Erin L. Allwein, Robert E. Schapire, Yoram Singer:
    Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers

We were glad to have Rob Schapire take a look back upon this great piece of work and discuss its influence on the community.