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Human in the Loop Learning (HiLL) Workshop at NeurIPS 2022
Shanghang Zhang · Hao Dong · Wei Pan · Pradeep Ravikumar · Vittorio Ferrari · Fisher Yu · Xin Wang · Zihan Ding

Fri Dec 02 06:30 AM -- 03:00 PM (PST) @ Room 396
Event URL: https://neurips-hill.github.io »

Recent years have witnessed the rising need for 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 interactive/active learning algorithms for real-world decision-making systems (e.g., autonomous driving vehicles, robotic systems, etc.), human-inspired learning that mitigates the gap between human intelligence and machine intelligence, human-machine collaborative learning that creates a more powerful learning system, lifelong learning that transfers knowledge to learn new tasks over a lifetime, 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 discussion on the interactive and collaborative learning between human and machine learning agents: Can they be organically combined to create a more powerful learning system? We believe the theme of the workshop will be of interest to broad NeurIPS attendees, especially those who are interested in interdisciplinary study.

Author Information

Shanghang Zhang (UC Berkeley)
Hao Dong (Peking University)
Wei Pan (Delft University of Technology)
Pradeep Ravikumar (Carnegie Mellon University)
Vittorio Ferrari (University of Edinburgh)
Fisher Yu (ETH Zurich)
Xin Wang (Microsoft Research)
Zihan Ding (Princeton University)

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