Principles of mixed-initiative user interfaces
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Predicting human interruptibility with sensors
ACM Transactions on Computer-Human Interaction (TOCHI)
Toward harnessing user feedback for machine learning
Proceedings of the 12th international conference on Intelligent user interfaces
Experience sampling for building predictive user models: a comparative study
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proactive learning: cost-sensitive active learning with multiple imperfect oracles
Proceedings of the 17th ACM conference on Information and knowledge management
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While many devices today increasingly have the ability to predict human activities, it is still difficult to build accurate personalized machine learning models. As users today will become responsible for helping to train their own models, we are interested in ways for applications to request labeled data from their users in a non-invasive way. This work focuses on opportunities for intelligent systems to ask their users for help through interactions over an extended period of time in order to improve their machine learning models. We focus on trading off the expected increase in accuracy with the potential interruptions that the questions may cause to improve the usability of such systems.