Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
How many queries are needed to learn?
Journal of the ACM (JACM)
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Machine Learning
Machine Learning
Preference Elicitation and Query Learning
The Journal of Machine Learning Research
Constraint-based optimization and utility elicitation using the minimax decision criterion
Artificial Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Preference elicitation with subjective features
Proceedings of the third ACM conference on Recommender systems
Learning complex concepts using crowdsourcing: a Bayesian approach
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
Interactive value iteration for Markov decision processes with unknown rewards
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Most models of utility elicitation in decision support and interactive optimization assume a predefined set of "catalog" features over which user preferences are expressed. However, users may differ in the features over which they are most comfortable expressing their preferences. In this work we consider the problem of feature elicitation: a user's utility function is expressed using features whose definitions (in terms of "catalog" features) are unknown. We cast this as a problem of concept learning, but whose goal is to identify only enough about the concept to enable a good decision to be recommended. We describe computational procedures for identifying optimal alternatives w.r.t. minimax regret in the presence of concept uncertainty; and describe several heuristic query strategies that focus on reduction of relevant concept uncertainty.