Virtual workshop, 17 or 18 July 2020 @ICML
This workshop aims to bring together researchers from academia and industry to discuss major challenges, outline recent advances, and highlight future directions pertaining to novel and existing large-scale real-world experiment design and active learning problems. We aim to highlight new and emerging research opportunities for the ML community that arise from the evolving needs to make experiment design and active learning procedures that are theoretically and practically relevant for realistic applications. Progress in this area has the potential to provide immense benefits in using experiment design and active learning algorithms in emerging high impact applications, such as material design, computational biology, algorithm configuration, AutoML, crowdsourcing, citizen science, robotics, and more.
Remark: In light of the COVID-19 situation, the workshop will be held virtually. For more information, please see the ICML conference website.
- Shipra Agrawal (Columbia University)
- Anca Dragan (UC Berkeley)
- Jennifer Listgarten (UC Berkeley)
- José Miguel Hernández Lobato (University of Cambridge)
- Pietro Perona (Caltech)
- Tom Rainforth (University of Oxford)
- Aaditya Ramdas (Carnegie Mellon University)
- Dorsa Sadigh (Stanford University)
- Angela Schoellig (University of Toronto)
Call for Submissions & Important Dates
Please see the Call for papers for submission instructions.
- Submission deadline: 15th June 2020, 11:59 PM (AoE time)
- Notification of acceptance: TBD (AoE time)
- Workshop date: 17th or 18th July 2020