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July 18

Virtual Conference



Professor at Princeton University

Professor at University of Toronto

Assistant professor at Stanford University

Professor at University of Toronto

Professor at Tsinghua University

Assistant professor at UC Berkeley

Postdoc Researcher at Facebook AI Research (FAIR)

Research Scientist at Carnegie Mellon University

Associate professor at Carnegie Mellon University

Assistant professor at UC Berkeley

Professor at

University of Tübingen


Agenda (TBD)

July 18


Call for Papers

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

  • Active/Interactive machine learning algorithms for autonomous decision-making systems,

  • Online learning and active learning,

  • Psychology driven human concept learning,

  • Explainable AI,

  • Systems for online and interactive learning algorithms,

  • Systems for collecting, preparing, and managing machine learning data,

  • Design, testing and assessment of interactive systems for data analytics,

  • Model understanding tools (debugging, visualization, introspection, etc).


These topics span a variety of scientific disciplines and application domains like machine learning, human-computer interaction, cognitive science, and robotics. It is an opportunity for scientists in these disciplines to share their perspectives, discuss solutions to common problems and highlight the challenges in the field to help guide future research. 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. 

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) formatted using the ICML template. All the submissions should be anonymous. The accepted papers are allowed to get submitted to other conference venues. 

Papers can be submitted through CMT:


Important Dates

Submission deadline: 25th June 2020 (23:59 AoE)

Acceptance notification: 10th, July 2020 (23:59 AoE)

Workshop Date: 18th July



Shanghang Zhang, UC Berkeley

Xin Wang, UC Berkeley

Fisher Yu, UC Berkeley

Jiajun Wu, Stanford University & Google

Trevor Darrell, UC Berkeley


2nd ICML Workshop on Human in the Loop Learning

Explainable AI and online active learning


2nd ICML Workshop on Human in the Loop Learning


July 18th, 2020


Virtual Conference

Contact Us

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