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Workshop
2nd ICML Workshop on Human in the Loop Learning (HILL)
Shanghang Zhang · Xin Wang · Fisher Yu · Jiajun Wu · Trevor Darrell

Sat Jul 18 11:00 AM -- 03:00 AM (PDT) @
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Event URL: https://www.icml-hill.com/ »

Recent years have witnessed the rising need for learning agents that can interact with humans. Such agents usually involve applications in computer vision, natural language processing, human computer interaction, and robotics. Creating and running such agents call for interdisciplinary research of artificial intelligence, machine learning, and software engineering design, which we abstract as Human in the Loop Learning (HILL). HILL is a modern machine learning paradigm of significant practical and theoretical interest. For HILL, models and humans engage in a two-way dialog to facilitate more accurate and interpretable learning. The workshop aims to bring together researchers and practitioners working on the broad areas of human in the loop learning, ranging from the interactive/active learning algorithm designs for real-world decision making systems (e.g., autonomous driving vehicles, robotic systems, etc.), models with strong explainability, as well as interactive system designs (e.g., data visualization, annotation systems, etc.). In particular, we aim to elicit new connections among these diverse fields, identifying theory, tools and design principles tailored to practical machine learning workflows. The target audience for the workshop includes people who are interested in using machines to solve problems by having a human be an integral part of the learning process. In this year’s HILL workshop, we emphasize on the interactive/active learning algorithms for real-world decision making systems as well as learning algorithms with strong explainability. We continue the previous effort to provide a platform for researchers to discuss approaches that bridge the gap between humans and machines and get the best of both worlds. We believe the theme of the workshop will be interesting to ICML attendees, especially those who are interested in interdisciplinary study.

Keywords: Active Learning · explainability · Human in the Loop Learning · Interactive learning · Interpretable learning · Interactive system designs

Author Information

Shanghang Zhang (UC Berkeley)
Xin Wang (UC Berkeley)
Fisher Yu (University of California, Berkeley)
Jiajun Wu (Stanford University)

Jiajun Wu is a Visiting Faculty Researcher at Google Research, New York City. In July 2020, he will join Stanford University as an Assistant Professor of Computer Science. He studies machine perception, reasoning, and its interaction with the physical world, drawing inspiration from human cognition.

Trevor Darrell (University of California at Berkeley)

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