Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Journal of Machine Learning Research
Discriminative learning for differing training and test distributions
Proceedings of the 24th international conference on Machine learning
Active learning with statistical models
Journal of Artificial Intelligence Research
Statistical active learning in multilayer perceptrons
IEEE Transactions on Neural Networks
Active learning of visual descriptors for grasping using non-parametric smoothed beta distributions
Robotics and Autonomous Systems
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We address the problem of batch active learning (or experiment design) in regression scenarios, where the best input points to label is chosen from a `pool' of unlabeled input samples. Existing active learning methods often assume that the model is correctly specified, i.e., the unknown learning target function is included in the model at hand. However, this assumption may not be fulfilled in practice (i.e., agnostic) and then the existing methods do not work well. In this paper, we propose a new active learning method that is robust against model misspecification. Simulations with various benchmark datasets as well as a real application to wafer alignment in semiconductor exposure apparatus illustrate the usefulness of the proposed method.