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Workshop
Real World Experiment Design and Active Learning
Ilija Bogunovic · Willie Neiswanger · Yisong Yue

Sat Jul 18 07:00 AM -- 03:35 PM (PDT) @ None
Event URL: https://realworldml.github.io/ »

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 machine learning community that arise from the evolving needs to make experiment design and active learning procedures that are theoretically and practically relevant for realistic applications.

The intended audience and participants include everyone whose research interests, activities, and applications involve experiment design, active learning, bandit/Bayesian optimization, efficient exploration, and parameter search methods and techniques. We expect the workshop to attract substantial interest from researchers working in both academia and industry. The research of our invited speakers spans both theory and applications, and represents a diverse range of domains where experiment design and active learning are of fundamental importance (including robotics & control, biology, physical sciences, crowdsourcing, citizen science, etc.).


The schedule is with respect to UTC (i.e., Universal Time) time zone.

Author Information

Ilija Bogunovic (ETH Zurich)
Willie Neiswanger (Carnegie Mellon University)
Yisong Yue (Caltech)

Yisong Yue is an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. He was previously a research scientist at Disney Research. Before that, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. He received a Ph.D. from Cornell University and a B.S. from the University of Illinois at Urbana-Champaign. Yisong's research interests lie primarily in the theory and application of statistical machine learning. He is particularly interested in developing novel methods for interactive machine learning and structured prediction. In the past, his research has been applied to information retrieval, recommender systems, text classification, learning from rich user interfaces, analyzing implicit human feedback, data-driven animation, behavior analysis, sports analytics, policy learning in robotics, and adaptive planning & allocation problems.

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