Information-based objective functions for active data selection
Neural Computation
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principles of mixed-initiative user interfaces
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning and reasoning about interruption
Proceedings of the 5th international conference on Multimodal interfaces
Examining the robustness of sensor-based statistical models of human interruptibility
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
BusyBody: creating and fielding personalized models of the cost of interruption
CSCW '04 Proceedings of the 2004 ACM conference on Computer supported cooperative work
Selective supervision: guiding supervised learning with decision-theoretic active learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A Scrutable User Modelling Infrastructure for Enabling Life-Long User Modelling
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Teaching-Learning by Means of a Fuzzy-Causal User Model
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Predictive student model supported by fuzzy-causal knowledge and inference
Expert Systems with Applications: An International Journal
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Predictive user models often require a phase of effortful supervised training where cases are tagged with labels that represent the status of unobservable variables. We formulate and study principles of lifelong learning where training is ongoing over a prolonged period. In lifelong learning, decisions about extending a case library are made continuously by balancing the cost of acquiring values of hidden states with the long-term benefits of acquiring new labels. We highlight key principles by extending BusyBody, an application that learns to predict the cost of interrupting a user. We transform the prior BusyBody system into a lifelong learner and then review experiments that highlight the promise of the methods.