Learning in the presence of malicious errors
SIAM Journal on Computing
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
An Approximate Nonmyopic Computation for Value of Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Conjunctive Concepts in Structural Domains
Machine Learning
Machine Learning
Machine Learning
Online feature elicitation in interactive optimization
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Version spaces: a candidate elimination approach to rule learning
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 1
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We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated according to (noisy) observations from experts, whose behaviors are modeled using discrete types. We propose recommendation techniques, inference methods, and query selection strategies to assist a user charged with choosing a configuration that satisfies some (partially known) concept. Our model is able to simultaneously learn the concept definition and the types of the experts. We evaluate our model with simulations, showing that our Bayesian strategies are effective even in large concept spaces with many uninformative experts.