IUI workshop on interactive machine learning

Authors:
Saleema Amershi;Maya Cakmak;W. Bradley Knox;Todd Kulesza;Tessa Lau
Affiliations:
Microsoft Research, Redmond, Washington, USA;Willow Garage, Inc., Menlo Park, California, USA;Massachusetts Institute of Technology, Cambridge, Massachusetts, USA;Oregon State University, Corvallis, Oregon, USA;Willow Garage, Inc., Menlo Park, California, USA
Venue:
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
Year:
2013

Citing 6
Cited 0

Integrating rich user feedback into intelligent user interfaces

Proceedings of the 13th international conference on Intelligent user interfaces
Active learning by labeling features

EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Towards maximizing the accuracy of human-labeled sensor data

Proceedings of the 15th international conference on Intelligent user interfaces
Interactive optimization for steering machine classification

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CueT: human-guided fast and accurate network alarm triage

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Designing Interactions for Robot Active Learners

IEEE Transactions on Autonomous Mental Development

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 Interactive Machine Learning (IML) 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 of IML.