The AutoML Cup is a competition that will challenge participants to design AutoML methods that perform well on a diverse set of tasks and data settings. It is a direct follow-up to the AutoML Decathlon 2022, comprising diverse, real-world problems whose domains are far afield from standard machine learning testbeds such as vision and language.
While the 2022 edition of the AutoML Decathlon competition explored the intersection of AutoML and data-centric AI along the axis of task diversity, this year’s AutoML Cup goes a step further by challenging competitors to cope with additional real-world data issues. With the key goal of maximizing scientific progress, the competition will feature several phases, similar to the impactful first AutoML challenge by ChaLearn. We will mandate participants to open-source their solutions after each phase in order to claim prizes, enabling participants to bootstrap from each others’ solutions. The phases will vary in data dimensions and resource settings (e.g., limited access to labeled training data).
Prizes
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Important Dates
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FAQ
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Organizers
- Sonia Cromp - University of Wisconsin-Madison
- Samuel Guo - Carnegie Mellon University
- Jeffrey Li - University of Washington
- Adrien Pavao - University of Paris-Saclay
- Nicholas Roberts - University of Wisconsin-Madison
- Spencer Schoenberg - University of Wisconsin-Madison
- Cong Xu - Hewlett Packard Enterprise
- Ameet Talwalkar - Carnegie Mellon University
- Frederic Sala - University of Wisconsin-Madison
Sponsors
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