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<div><b>Call for Participation:</b> Machine Learning from User Interactions (MLUI) workshop at IEEE VIS 2020<br></div><div><br></div><div><b>Workshop Date:</b> October 25 or 26, 2020</div><div><b>Workshop URL:</b> <a href="http://learningfromusersworkshop.github.io" target="_blank">learningfromusersworkshop.github.io</a></div><div><b>Conference Status:</b> IEEE VIS has announced that they will be a fully virtual conference this year in response to the COVID outbreak.<br></div><div><br></div><div><b>Papers Submission deadline:</b> July 24, 2020<br><b>Author notification: </b> August 12, 2020
</div><div><br></div>The
Machine Learning from User Interactions (MLUI) workshop seeks to bring
together researchers to share their knowledge and build collaborations
at the intersection of the Machine Learning and Visualization fields,
with a focus on learning from user interaction. Rather than focusing on
what visualization can do to support machine learning (as in current
Explainable AI research), this workshop seeks contributions on how
machine learning can support visualization. Such support incorporates
human-centric sensemaking processes, user-driven analytical systems, and
gaining insight from data. Our intention in this workshop is to
generate open discussion about how we currently learn from user
interaction, how to build intelligent visualization systems, and how to
proceed with future research in this area. We hope to foster discussion
regarding systems, interaction models, and interaction techniques.
Further, we hope to extend last year’s collaborative creation of a
research agenda that explores the future of machine learning with user
interaction.<br><br><div>We invite research and position papers between 5 and 10 pages in length
(NOT including references). All submissions must be formatted according
to the VGTC conference style template (i.e., NOT the journal style
template that full papers use). All papers accepted for presentation at
the workshop will be published and linked from the workshop website. All
papers should contain full author names and affiliations. These papers
are considered archival; reuse of the content in a follow-up publication
is only permitted in a proper journal, and any extended version must
extend the original paper by at least 30%. If applicable, a link to a
short video (up to 5 min. in length) may also be submitted. The papers
will be juried by the organizers and selected external reviewers and
will be chosen according to relevance, quality, and likelihood that they
will stimulate and contribute to the discussion. At least one author of
each accepted paper needs to register for the conference (even if only
for the workshop).
Papers should be submitted to the "<a href="https://new.precisionconference.com/submissions" rel="nofollow" target="_blank">VIS 2020 MLUI 2020</a>" track in PCS under the VGTC Society. <br></div><div><br></div><div>Relevant topics include but are not limited to:</div><div>
<ul><li>How are machine learning algorithms currently learning from user interaction, and what other possibilities exist?</li><li>What kinds of interactions can provide feedback to machine learning algorithms?</li><li>What can machine learning algorithms learn from interactions?</li><li>Which machine learning algorithms are most applicable in this domain?</li><li>How can machine learning algorithms be designed to enable user interaction and feedback?</li><li>How can visualizations and interactions be designed to exploit machine learning algorithms?</li><li>How can visualization system architectures be designed to support machine learning?</li><li>How should we manage conflicts between the user's intent and the data or machine learning algorithm capabilities?</li><li>How can we evaluate systems that incorporate both machine learning algorithms and user interaction together?</li><li>How can machine learning and user interaction together make both computation and user cognition more efficient?</li><li>How can we support the sensemaking process by learning from user interaction?</li></ul>
</div><div><b><br></b></div><div><b>Organizers</b></div><div><ul><li>John Wenskovitch, Pacific Northwest National Lab and Virginia Tech</li><li>Michelle Dowling, Grand Valley State University</li><li>Eli T. Brown, DePaul University</li><li>Kris Cook, Pacific Northwest National Lab</li><li>Ab Mosca, Tufts University</li><li>Conny Walchshofer, Johannes Kepler University Linz<br></li><li>Marc Streit, Johannes Kepler University Linz</li><li>Kai Xu, Middlesex University</li></ul></div><div><br></div><div><b>Steering Committee</b></div><div><ul><li>Chris North, Virginia Tech</li><li>Remco Chang, Tufts University</li><li>Alex Endert, Georgia Tech</li><li>David H. Rogers, Los Alamos National Lab<br></li></ul></div><div><br>
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