Neural networks and the bias/variance dilemma
Neural Computation
Information-based objective functions for active data selection
Neural Computation
Some label efficient learning results
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Making Rational Decisions Using Adaptive Utility Elicitation
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A POMDP formulation of preference elicitation problems
Eighteenth national conference on Artificial intelligence
Prediction, Learning, and Games
Prediction, Learning, and Games
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Worst-Case Analysis of Selective Sampling for Linear Classification
The Journal of Machine Learning Research
Importance weighted active learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Active learning with statistical models
Journal of Artificial Intelligence Research
Agent-based micro-storage management for the Smart Grid
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Putting the 'smarts' into the smart grid: a grand challenge for artificial intelligence
Communications of the ACM
Minimizing regret with label efficient prediction
IEEE Transactions on Information Theory
Partial monitoring with side information
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
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A key issue for the realization of the smart grid vision is the implementation of effective demand-side management. One possible approach involves exposing dynamic energy prices to end-users. In this paper, we consider a resulting problem on the user's side: how to adaptively heat a home given dynamic prices. The user faces the challenge of having to react to dynamic prices in real time, trading off his comfort with the costs of heating his home to a certain temperature. We propose an active learning approach to adjust the home temperature in a semi-automatic way. Our algorithm learns the user's preferences over time and automatically adjusts the temperature in real-time as prices change. In addition, the algorithm asks the user for feedback once a day. To find the best query time, the algorithm solves an optimal stopping problem. Via simulations, we show that our algorithm learns users' preferences quickly, and that using the expected utility loss as the query criterion outperforms standard approaches from the active learning literature.