Active Learning for Misspecified Models

Active learning is the problem in supervised learning to design the locations oftraining input points so that the generalization error is minimized. Existing active learning methods often assume that the model used for learning is correctly specified, i.e., the learning target function can be expressed bythe model at hand. In many practical situations, however, this assumption may not be fulfilled. In this paper, we first show that the existing activelearning method can be theoretically justified under slightly weaker condition: the model does not have to be correctly specified, but slightly misspecified models are also allowed. However, it turns out that the weakened condition is still restrictive in practice.