Many applications of Machine Learning (ML) involve interactions with humans. Humans may provide input to a learning algorithm (in the form of labels, demonstrations, corrections, rankings or evaluations) while observing its outputs (in the form of feedback, predictions or executions). Although humans are an integral part of the learning process, traditional ML systems used in these applications are agnostic to the fact that inputs/outputs are from/for humans.

However, a growing community of researchers at the intersection of ML and human-computer interaction are making interaction with humans a central part of developing ML systems. These efforts include applying interaction design principles to ML systems, using human-subject testing to evaluate ML systems and inspire new methods, and changing the input and output channels of ML systems to better leverage human capabilities. With this workshop at IUI 2013 we aim to bring this community together to share ideas, get up-to-date on recent advances, progress towards a common framework and terminology for the field, and discuss the open questions and challenges.

Topics relevant for the workshop include:
  • End-user programming
  • Active learning
  • Reinforcement learning with human feedback or guidance
  • Interactive clustering
  • Feature labeling
  • Programming by demonstration
  • Transparency and feedback in ML
  • Empirical studies and computational models of human teaching
  • Democratizing ML
  • Human-in-the-loop intelligent systems

Invited Speakers

Rich Caruana - Microsoft Research (View talk / View slides)
Rebecca Fiebrink - Princeton University (View talk / View slides)
Chad Jenkins - Brown University (View talk / View slides)
Ashish Kapoor - Microsoft Research (View talk)
Edith Law - Harvard
Henry Lieberman - MIT (View talk / View slides)
Reid Porter - Los Alamos National Lab (View talk / View slides)

Important dates

January 9th, 2013 - Abstract submission deadline
February 11, 2013 - Notification of acceptance
February 25, 2013 - Camera-ready submission deadline
March 19, 2013 - Workshop in Santa Monica, CA

Author information

We invite contributions in the form of 2-page abstracts that summarize ongoing or recent research related to Interactive ML or describe potential application domains for Interactive ML. Abstracts may describe previously published work. Selection will be based on the relevance and quality of the work and will aim at balancing perspectives and backgrounds of participants. Accepted abstracts will be presented as a poster and a short spotlight talk prior to the poster session.

Formatting: Please use the ACM Extended Abstract format
Submissions: Please submit your abstracts at the EasyChair IMLW submission page
Poster dimensions: 48 inches by 36 inches (either landscape or portrait orientation)

Organizing Committee

Saleema Amershi, Microsoft Research
Maya Cakmak, Willow Garage
W. Bradley Knox, MIT
Todd Kulesza, Oregon State University
Tessa Lau, Willow Garage

Program Committee

Luis Carlos Cobo, Georgia Institute of Technology
Nick DePalma, MIT
Hayley Hung, University of Amsterdam
Kshitij Judah, Oregon State University
George Konidaris, MIT
Brian Lim, Fraunhofer CSE
Manuel Lopes, INRIA
Andreas Paepcke, Stanford University
Stephanie Rosenthal, Bossa Nova Robotics
Simone Stumpf, City University London
Bener Suay, WPI
Kaushik Subramanian, Georgia Institute of Technology
James E. Young, University of Manitoba