Palais des Congrès de Montréal Convention and Exhibition Center Room 512 e [Floor map] Talk abstracts Morning session (9:00 am-12:00 pm) 9:00 - Welcome and introduction. Evelyne Viegas [slides] 9:10 - Invited talk, Challenges in Medical Image Analysis: Comparison, Competition, Collaboration, Bram van Ginneken [slides] 9:50 - Break 10:20 - Invited talk, Techniques and Technologies for Efficient and Realistic Benchmarks: Examples from the MediaEval Multimedia Benchmark and CLEF NewsREEL, Martha Larson [slides] 11:00 - Discussion: Open Innovation, Balazs Kegl and Ben Hamner moderators [Balazs' post][Balazs' slides] 12:00 - Break Break-out session on AutoML challenge. 12:30 - Presentation of the AutoML challenge. Isabelle Guyon -- Announcement of the new GPU track. [slides] 13:00 - Automated Machine Learning: Successes & Challenges. Frank Hutter. Team aaad_freiburg. First place AutoML1 phase, second place AutoML2 phase. [paper][supplementary material][slides] 13:30 - Sensible allocation of computation for ensemble construction. James Lloyd. Team jrl44/backstreet.bayes. First place AutoML2 phase, second place AutoML1 phase. [slides] 14:00 - Scalable ensemble learning with stochastic feature boosting. Eugene Tuv. Team ideal.intel.analytics. First place Final0 phase, second place Final1 phase. [slides] 14:30 - Break Afternoon session (15:00-18:30) 15:00 - Invited talk, Lessons Learned from the PASCAL VOC Challenges, and Improving the Data Analytics Process, Chris Williams [slides]
15:40 - Discussion: Coopetitions, Evelyne Viegas and Isabelle Guyon moderator 16:40 - Break 17:00 - Contributed talk, Academic Torrents: Scalable Data Distribution, Henry Z. Lo and Joseph Paul Cohen [paper][slides]
17:30 - Open discussion, Michele Sebag modelator 18:20 - Wrap up 18:30 - Adjourn
We are connected to the Bayesian Optimization workshop and the Black Box Learning and Inference workshop, because they both treat in some way the "Automatic Machine Learning" problem, which we will discuss during the lunch session. |
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