The 29th ACM SIGKDD Conference on Knowledge, Discovery, and Data Mining was held August 6th through 10th in Long Beach, California. This annual conference brings together the leading community for data mining, data science, and analytics. At this year’s conference, the 2013 paper “Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms” was awarded the ACM SIG-KDD Research Track Test of Time Award. The SIGKDD Test of Time award recognizes outstanding papers from past KDD Conferences beyond the last decade that have had an important impact on the data mining research community. Co-authored by Chris Thornton (a UBC Master’s student at the time) and Dr. Frank Hutter (a UBC postdoc at the time, now a professor at University of Freiburg), under the supervision of UBC CS professors Dr. Holger Hoos (now a professor at Aachen University in Germany) and Dr. Kevin Leyton-Brown (a UBC computer science professor), this paper has been recognized for the incredible impact it has had on the rapid progress of Machine Learning throughout this past decade.
Our own Dr. Kevin Leyton-Brown has expanded more on the honour of this achievement in this UBC article. A huge congratulations to Chris Thornton, Dr. Frank Hutter, Dr. Holger Hoos and Dr. Kevin Leyton Brown.
You can see the other KDD 2023 Award Recipients here.