We're excited to announce the 2nd Human In the Loop Learning Workshop at ICML 2020!  We invite submission of relevant, four to eight-page papers before the June 1st (anywhere on earth) deadline.  

Website: https://www.icml-hill.com/

Call for Papers:

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. 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 where researchers can discuss approaches that bridge the gap between humans and machines and get the best of both worlds. 

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).

Important Dates:

* Submission deadline: June 1, 2020 (anywhere on earth)

* Notification: Around June 20, 2020

* Workshop: July 17 or 18, 2020

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/HILL2020. The accepted papers are allowed to get submitted to other conference venues. 

Organizers

* Shanghang Zhang, UC Berkeley

* Xin Wang,  UC Berkeley

* Fisher Yu,  UC Berkeley

* Jiajun Wu, Stanford & Google

* Trevor Darrell, UC Berkeley