Date and Location: October 22, 2018, 9:00am - 12:40 pm, in Paris Room at IEEE VIS
The goal of this workshop is to bring together researchers from across the VIS community – SciVis, InfoVis, and VAST – 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. Our intention in this workshop is to pull expertise from across all fields of VIS in order to generate open discussion about how we currently learn from user interaction and where we can go with future research in this area. We hope to foster discussion regarding systems, interaction models, and interaction techniques across fields within the VIS community, rather than the current state of having these discussions independently contained within the SciVis/InfoVis/VAST fields. Further, we hope to collaboratively create a research agenda that explores the future of machine learning with user interaction based on the discussion during the workshop.
Session 1: Applications (9:00am - 10:40am)
- 9:00am: Introduction and Welcome
- 9:10am: HyperTuner: Visual Analytics for Hyperparameter Tuning by Professionals by Tianyi Li, Gregorio Convertino, Wenbo Wang, Haley Most, Tristan Zajonc, Yi-Hsun Tsai
- 9:20am: Computer-supported Interactive Assignment of Keywords for Literature Collections by Shivam Agarwal, Jürgen Bernard, Fabian Beck
- 9:30am: Using Hidden Markov Models to Determine Cognitive States of Visual Analytic Users by Mohamad Aboufoul, Ryan Wesslen, Isaac Cho, Wenwen Dou, Samira Shaikh
- 9:40am: Providing Contextual Assistance in Response to Frustration in Visual Analytics Tasks by Prateek Panwar, Adam Bradley, Christopher Collins
- 9:50am: Interactive Machine Learning Heuristics by Eric Corbett, Nathaniel Saul, Meg Pirrung
- 9:55am: Opening the Black-Box: Towards more Interactive and Interpretable Machine Learning by Fabian Peña, John Guerra-Gomez
- 10:00am: Discussion: What new applications, capabilities, and opportunities could be enabled by machine learning from user interaction?
- 10:40am: Break
Session 2: Research Agenda (11:00am - 12:40pm)
- 11:00am: ModelSpace: Visualizing the Trails of Data Models in Visual Analytics Systems by Eli T. Brown, Sriram Yarlagadda, Kristin Cook, Remco Chang, Alex Endert
- 11:10am: A Human-in-the-Loop Software Platform by Fang Cao, David Scroggins, Lebna Thomas, Eli T. Brown
- 11:20am: A Bidirectional Pipeline for Semantic Interaction by Michelle Dowling, John Wenskovitch, Peter Hauck, Adam Binford, Nicholas Polys, Chris North (Supplementary Content)
- 11:30am: Speculative Execution for Guided Visual Analytics by Fabian Sperrle, Jürgen Bernard, Michael Sedlmair, Daniel Keim, Mennatallah El-Assady
- 11:40am: Discussion: What research is needed to enable the identified applications, capabilities, and opportunities?
- 12:20am: Next Steps: Planning the outcomes of the workshop
- 12:40am: Workshop Concludes …but go have lunch with each other and keep talking :)
The topic of the workshop will focus on issues and opportunities related to the use of machine learning to learn from user interaction in the course of data visualization and analysis. Specifically, we will focus on research questions including:
- How are machine learning algorithms currently learning from user interaction, and what other possibilities exist?
- What kinds of interactions can provide feedback to machine learning algorithms?
- What can machine learning algorithms learn from interactions?
- Which machine learning algorithms are most applicable in this domain?
- How can machine learning algorithms be designed to enable user interaction and feedback?
- How can visualizations and interactions be designed to exploit machine learning algorithms?
- How can visualization system architectures be designed to support machine learning?
- How should we manage conflicts between the user’s intent and the data or machine learning algorithm capabilities?
- How can we evaluate systems that incorporate both machine learning algorithms and user interaction together?
- How can machine learning and user interaction together make both computation and user cognition more efficient?
- How can we support the sensemaking process by learning from user interaction?
We have two submission tracks: for papers and for posters.
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). Papers are to be submitted online through the Precision Conference System at the Machine Learning from User Interaction for Visualization and Analytics track. All papers accepted for presentation at the workshop will be published on IEEE Xplore and linked from the workshop website. All papers should contain full author names and affiliations. 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). Registration information will be available on the IEEE VIS website.
June 30, 2018 July 15, 2018
July 31, 2018 August 6, 2018
Camera-ready deadline: August 20, 2018
Speaker Schedule Available: September 15, 2018
Workshop: October 22, 2018, 9:00 AM
We invite both late-breaking work and contributions in this area from other research domains to submit extended abstracts between 2 and 4 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). Extended abstracts are to be submitted via email to our GMail account: firstname.lastname@example.org. All abstracts accepted for presentation at the workshop will be published on IEEE Xplore and linked from the workshop website. All abstracts should contain full author names and affiliations. If applicable, a link to a short video (up to 5 min. in length) may also be submitted. The abstracts 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 poster needs to register for the conference (even if only for the workshop). Registration information will be available on the IEEE VIS website.
Submission deadline: August 15, 2018
Author notification: September 1, 2018
Camera-ready deadline: October 1, 2018
Workshop: October 22, 2018, 9:00 AM
John Wenskovitch, Virginia Tech (email@example.com)
Michelle Dowling, Virginia Tech (firstname.lastname@example.org)
Chris North, Virginia Tech
Remco Chang, Tufts University
Alex Endert, Georgia Tech
David Rogers, Los Alamos National Lab