Peridot: creating user interfaces by demonstration
Watch what I do
Eager: programming repetitive tasks by demonstration
Watch what I do
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Programming by demonstration: an inductive learning formulation
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
Using plan recognition in human-computer collaboration
UM '99 Proceedings of the seventh international conference on User modeling
Instructible information agents for Web mining
Proceedings of the 5th international conference on Intelligent user interfaces
Task Analysis for Human-Computer Interaction
Task Analysis for Human-Computer Interaction
Machine Learning
Machine Learning
Machine Learning
Version Space Algebra and its Application to Programming by Demonstration
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Disciple-COA: From Agent Programming to Agent Teaching
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An Improved On-line Algorithm for Learning Linear Evaluation Functions
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Multiple selections in smart text editing
Proceedings of the 7th international conference on Intelligent user interfaces
Adaptive interfaces and agents
The human-computer interaction handbook
Programming by Demonstration Using Version Space Algebra
Machine Learning
a CAPpella: programming by demonstration of context-aware applications
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Staging transformations for multimodal web interaction management
Proceedings of the 13th international conference on World Wide Web
Recovering from errors during programming by demonstration
Proceedings of the 13th international conference on Intelligent user interfaces
Automatically personalizing user interfaces
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Minimizing user effort in transforming data by example
Proceedings of the 19th international conference on Intelligent User Interfaces
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Applications of machine learning can be viewed as teacher-student interactions in which the teacher provides training examples and the student learns a generalization of the training examples. One such application of great interest to the IUI community is adaptive user interfaces. In the traditional learning interface, the scope of teacher-student interactions consists solely of the teacher/user providing some number of training examples to the student/learner and testing the learned model on new examples. Active learning approaches go one step beyond the traditional interaction model and allow the student to propose new training examples that are then solved by the teacher. In this paper, we propose that interfaces for machine learning should even more closely resemble human teacher-student relationships. A teacher's time and attention are precious resources. An intelligent student must proactively contribute to the learning process, by reasoning about the quality of its knowledge, collaborating with the teacher, and suggesting new examples for her to solve. The paper describes a variety of rich interaction modes that enhance the learning process and presents a decision-theoretic framework, called DIAManD, for choosing the best interaction. We apply the framework to the SMARTedit programming by demonstration system and describe experimental validation and preliminary user feedback.