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Workshop
ICML Workshop on Human in the Loop Learning (HILL)
Trevor Darrell · Xin Wang · Li Erran Li · Fisher Yu · Zeynep Akata · Wenwu Zhu · Pradeep Ravikumar · Shiji Zhou · Shanghang Zhang · Kalesha Bullard

Sat Jul 24 04:15 AM -- 11:10 AM (PDT) @ None
Event URL: https://www.icml-hill.com/ »

Recent years have witnessed the rising need for the machine learning systems that can interact with humans in the learning loop. Such systems can be applied to computer vision, natural language processing, robotics, and human computer interaction. Creating and running such systems call for interdisciplinary research of artificial intelligence, machine learning, and software engineering design, which we abstract as Human in the Loop Learning (HILL). The HILL workshop aims to bring together researchers and practitioners working on the broad areas of HILL, ranging from the interactive/active learning algorithms for real-world decision making systems (e.g., autonomous driving vehicles, robotic systems, etc.), lifelong learning systems that retain knowledge from different tasks and selectively transfer knowledge to learn new tasks over a lifetime, models with strong explainability, as well as interactive system designs (e.g., data visualization, annotation systems, etc.). The HILL workshop continues the previous effort to provide a platform for researchers from interdisciplinary areas to share their recent research. In this year’s workshop, a special feature is to encourage the debate between HILL and label-efficient learning: Are these two learning paradigms contradictory with each other, or can they be organically combined to create a more powerful learning system? We believe the theme of the workshop will be of interest for broad ICML attendees, especially those who are interested in interdisciplinary study.

Author Information

Trevor Darrell (University of California at Berkeley)
Xin Wang (UC Berkeley)
Li Erran Li (AWS AI, Amazon)
Fisher Yu (University of California, Berkeley)
Zeynep Akata (University of Tübingen)

Zeynep Akata is a professor of Computer Science (W3) within the Cluster of Excellence Machine Learning at the University of Tübingen. After completing her PhD at the INRIA Rhone Alpes with Prof Cordelia Schmid (2014), she worked as a post-doctoral researcher at the Max Planck Institute for Informatics with Prof Bernt Schiele (2014-17) and at University of California Berkeley with Prof Trevor Darrell (2016-17). Before moving to Tübingen in October 2019, she was an assistant professor at the University of Amsterdam with Prof Max Welling (2017-19). She received a Lise-Meitner Award for Excellent Women in Computer Science from Max Planck Society in 2014, a young scientist honour from the Werner-von-Siemens-Ring foundation in 2019 and an ERC-2019 Starting Grant from the European Commission. Her research interests include multimodal learning and explainable AI.

Wenwu Zhu (Tsinghua University)

Wenwu Zhu is currently a Professor of Computer Science Department of Tsinghua University and Vice Dean of National Research Center on Information Science and Technology. Prior to his current post, he was a Senior Researcher and Research Manager at Microsoft Research Asia. He was the Chief Scientist and Director at Intel Research China from 2004 to 2008. He worked at Bell Labs New Jersey as a Member of Technical Staff during 1996-1999. He has been serving as the chair of the steering committee for IEEE T-MM since January 1, 2020. He served as the Editor-in-Chief for the IEEE Transactions on Multimedia (T-MM) from 2017 to 2019. And Vice EiC for IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) from 2020-2021 He served as co-Chair for ACM MM 2018 and co-Chair for ACM CIKM 2019. His current research interests are in the areas of multimodal big data and intelligence, and multimedia networking. He received 10 Best Paper Awards. He is a member of Academia Europaea, an IEEE Fellow, AAAS Fellow, and SPIE Fellow.

Pradeep Ravikumar (CMU)
Shiji Zhou (Tsinghua University)
Shanghang Zhang (UC Berkeley)
Kalesha Bullard (Facebook AI Research)

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