Call for paper – ICML2021 Human In the Loop Learning Workshop

Hi all, 

We're excited to announce the 3rd Human In the Loop Learning Workshop at ICML 2021!  We invite submission of relevant, four to eight-page papers before the June 10th (AoE) deadline.  

Workshop Website: https://sites.google.com/view/hill2021 

Call for Papers:

Recent years have witnessed the rising need for the machine learning systems that have 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 cognitive science, 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 human-inspired learning. 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 exploration of human-inspired learning. We believe the theme of the workshop will be of interest for broad ICML attendees, especially those who are interested in interdisciplinary study. 

We welcome high-quality submissions on theory, algorithms and system designs in the broad area of human in the loop learning. A few (non-exhaustive) topics of interest include:

• • Interactive/Active machine learning algorithms for autonomous decision-making systems,
• • Lifelong learning systems that learn a sequence of tasks and leverage their shared structure to enable knowledge transfer over a lifetime,
• • Online learning and active learning,
• • Comparison of human in the loop learning and label-efficient learning,
• • Psychology driven human concept learning,
• • Explainable AI,
• • Human-inspired learning, 
• • Design, testing and assessment of interactive systems for data analytics,
• • Model understanding tools (debugging, visualization, introspection, etc.).

Important Dates:

* Submission deadline: June 10, 2021 (anywhere on earth)

* Notification: Around June 28, 2021

* Workshop: July 23 or 24, 2021

Submission instructions:

We invite submissions of full papers as well as works-in-progress, position papers, and papers describing open problems and challenges. While original contributions are preferred, we also invite submissions of high-quality work that has recently been published in other venues or is concurrently submitted. We encourage creative ML approaches, as well as interdisciplinarity and perspectives from outside traditional ML. Papers should be 4-8 pages in length (excluding references and acknowledgments) formatted using the ICML template and submitted online via the link available at https://cmt3.research.microsoft.com/HILL2021. The accepted papers are allowed to get submitted to other conference venues. 

Organizers:       

Xin Wang, PhD at UC Berkeley,

Shiji Zhou, PhD at Tsinghua University,

Shanghang Zhang, Postdoc. at UC Berkeley,

Fisher Yu, Assistant professor at ETH Zurich,

Trevor Darrell, Professor at UC Berkeley,

Li Erran Li, Senior applied scientist at Amazon,

Been Kim, S. Research Scientist at Google Brain,

Wenwu Zhu, Professor at Tsinghua University, 

Kalesha Bullard, Postdoc. Researcher at F